{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 正式赛题——船运到达时间预测\n",
    "在企业全球化业务体系中，海运物流作为其最重要的一项支撑。其中，船运公司会和数据供应公司进行合作，对运输用的船通过GPS进行定位以监控船的位置；在运输管理的过程中，货物到达目的港的时间是非常重要的一项数据，那么需要通过船运的历史数据构建模型，对目的港到达时间进行预测，预测时间简称为ETA（estimated time of arrival），目的港到达时间预测为ARRIVAL_ETA。\n",
    "本次大赛提供历史运单GPS数据、历史运单事件数据、港口坐标数据，**预测货物运单的到达时间，对应“历史运单事件”数据中EVENT_CODE字段值为ARRIVAL AT PORT时EVENT_CONVOLUTION_DATE的时间值**。\n",
    "\n",
    "一、比赛数据\n",
    "大赛提供脱敏后的训练数据及测试数据，训练数据集包括：历史运单GPS数据、历史运单事件数据、港口坐标数据，这些数据主要用于参赛队伍训练模型，制定预估策略；测试运单数据为不同运单、运输过程中的不同位置所构成，供选手测试对应的ETA时间。\n",
    "货物运单在船运过程中，会产生大量的GPS运单数据，记录为“历史运单GPS数据”；货物运单在船运过程中离开起运港、到达中转港、到达目的港等关键事件，记录为“历史运单事件数据”；“港口的坐标数据“为与运单船运相关的港口坐标信息。\n",
    "允许选手合理增加与题目相关的外部数据进行纠正，如大赛提供的港口坐标数据存在偏差时可自行补充数据纠正"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 坐标数据描述每个运单在船运的过程中涉及的港口位置信息:\n",
    "- 港口名称\n",
    "- 港口的经度坐标\n",
    "- 港口的纬度坐标\n",
    "- 国家\n",
    "- 省|州\n",
    "- 城市\n",
    "- 县|区\n",
    "- 详细地址。\n",
    "- 港口编码，即港口的字母简码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>PORT_CODE</th>\n",
       "      <th>TRANSPORT_NODE_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2456.000000</td>\n",
       "      <td>2456.000000</td>\n",
       "      <td>2.500000e+01</td>\n",
       "      <td>2.420000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.518344</td>\n",
       "      <td>18.550666</td>\n",
       "      <td>3.458640e+07</td>\n",
       "      <td>5.477894e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>71.399595</td>\n",
       "      <td>22.590817</td>\n",
       "      <td>7.384261e+07</td>\n",
       "      <td>7.974048e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-175.183605</td>\n",
       "      <td>-53.793502</td>\n",
       "      <td>2.462000e+06</td>\n",
       "      <td>1.170000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.457919</td>\n",
       "      <td>0.611022</td>\n",
       "      <td>2.576000e+06</td>\n",
       "      <td>2.927750e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.676975</td>\n",
       "      <td>18.432586</td>\n",
       "      <td>3.519000e+06</td>\n",
       "      <td>3.553500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.510925</td>\n",
       "      <td>37.748396</td>\n",
       "      <td>3.684000e+06</td>\n",
       "      <td>9.520150e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>178.557549</td>\n",
       "      <td>73.212735</td>\n",
       "      <td>2.118270e+08</td>\n",
       "      <td>2.842170e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         LONGITUDE     LATITUDE     PORT_CODE  TRANSPORT_NODE_ID\n",
       "count  2456.000000  2456.000000  2.500000e+01       2.420000e+03\n",
       "mean     16.518344    18.550666  3.458640e+07       5.477894e+07\n",
       "std      71.399595    22.590817  7.384261e+07       7.974048e+07\n",
       "min    -175.183605   -53.793502  2.462000e+06       1.170000e+05\n",
       "25%      -3.457919     0.611022  2.576000e+06       2.927750e+06\n",
       "50%       7.676975    18.432586  3.519000e+06       3.553500e+06\n",
       "75%      57.510925    37.748396  3.684000e+06       9.520150e+07\n",
       "max     178.557549    73.212735  2.118270e+08       2.842170e+08"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#港口坐标数据\n",
    "#港口坐标数据描述每个运单在船运的过程中涉及的港口位置信息。\n",
    "port=pd.read_csv('../event_port/port.csv')\n",
    "port.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 历史运单GPS数据\n",
    "#### 历史运单GPS数据描述每个运单在船运的过程中，所在船产生的GPS位置的相关信息。\n",
    "- loadingOrder 脱敏后的主运单，货物的运单编号，类似快递单号\n",
    "- carrierName 脱敏后的承运商名称，类似快递公司名称\n",
    "- timestamp 时间，格式为：yyyy-MM-dd'T'HH:mm:ss.SSSZ，如2019-09-05T16:33:17.000Z\n",
    "- longitude 货物在运输过程中，当前船舶所处的经度坐标，如114.234567\n",
    "- latitude 货物在运输过程中，当前船舶所处的纬度坐标，如21.234567\n",
    "- vesselMMSI 脱敏后的船舶海上移动业务识别码MMSI， 唯一标识，对应到每一艘船\n",
    "- speed 单位km/h，货物在运输过程中，当前船舶的瞬时速度，部分数据未提供的可自行计算。\n",
    "- direction 当前船舶的行驶方向，正北是0度，31480代表西北方向314.80度，900代表正北偏东9度。\n",
    "- vesselNextport 船舶将要到达的下一港口，港口名称可能不规范，如CNQIN、CN QIN、CN QINGDAO都代表下一站为中国青岛港口。\n",
    "- vesselNextportETA 船运公司给出的到“下一个港口”预计到达时间，格式为：yyyy-MM-dd'T'HH:mm:ss.SSSZ，如2019-09-12T16:33:17.000Z\n",
    "- vesselStatus 当前船舶航行状态，主要包括：\n",
    "  - moored\n",
    "  - under way using engine\n",
    "  - not under command\n",
    "  - at anchor\n",
    "  - under way sailing\n",
    "  - constrained by her draught\n",
    "- vesselDatasource 船舶数据来源（岸基/卫星）：Coastal AIS，Satellite\n",
    "- TRANSPORT_TRACE  船的路由，由“-”连接组成，例如CNSHK-MYPKG-MYTPP。由承运商预先录入，实际小概率存在不按此路由行驶（如遇塞港时），但最终会到达目的港口。\n",
    "\n",
    "### 注意!!: 一个运单对应一艘船，一艘船可以对应多个运单"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一列一列读取GPS数据分析\n",
    "- 总共147907541条数据\n",
    "- 运单数量:21157条;相应的event中运单数量15512;因此gps中很多订单是没有event数据的\n",
    "- 时间库跨度:1.1~4.30总共4个月\n",
    "- TRANSPORT_TRACE中，有55029818缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.194400e+07</td>\n",
       "      <td>7.194400e+07</td>\n",
       "      <td>7.194400e+07</td>\n",
       "      <td>7.194400e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.754967e+01</td>\n",
       "      <td>1.283384e+01</td>\n",
       "      <td>2.180132e+01</td>\n",
       "      <td>2.059113e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.017714e+01</td>\n",
       "      <td>2.228892e+01</td>\n",
       "      <td>1.314887e+01</td>\n",
       "      <td>1.009879e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.800000e+02</td>\n",
       "      <td>-5.100285e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.550702e+01</td>\n",
       "      <td>1.284487e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.120000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.829897e+01</td>\n",
       "      <td>1.376067e+01</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>2.350000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.048785e+02</td>\n",
       "      <td>3.090405e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>2.891000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.800000e+02</td>\n",
       "      <td>6.859553e+01</td>\n",
       "      <td>1.890000e+02</td>\n",
       "      <td>5.110000e+04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude         speed     direction\n",
       "count  7.194400e+07  7.194400e+07  7.194400e+07  7.194400e+07\n",
       "mean   5.754967e+01  1.283384e+01  2.180132e+01  2.059113e+04\n",
       "std    7.017714e+01  2.228892e+01  1.314887e+01  1.009879e+04\n",
       "min   -1.800000e+02 -5.100285e+01  0.000000e+00 -1.000000e+00\n",
       "25%    1.550702e+01  1.284487e+00  1.200000e+01  1.120000e+04\n",
       "50%    7.829897e+01  1.376067e+01  2.700000e+01  2.350000e+04\n",
       "75%    1.048785e+02  3.090405e+01  3.200000e+01  2.891000e+04\n",
       "max    1.800000e+02  6.859553e+01  1.890000e+02  5.110000e+04"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gpsdf.shape\n",
    "#gpsdf.to_csv('gps_sample5kw.csv',index=False)\n",
    "gpsdf=pd.read_csv('../clean_dataset/dataHasAllXY.csv')\n",
    "gpsdf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数据进行预处理\n",
    "def get_data(data, mode='train'):\n",
    "    \n",
    "    assert mode=='train' or mode=='test'\n",
    "    \n",
    "    if mode=='train':#船舶将要到达的下一个港口的预计时间,转码格式\n",
    "        pass\n",
    "    elif mode=='test':\n",
    "        data['temp_timestamp'] = data['timestamp']#当前时间\n",
    "        data['onboardDate'] = pd.to_datetime(data['onboardDate'], infer_datetime_format=True)#离开起运港时间\n",
    "    data['timestamp'] = pd.to_datetime(data['timestamp'], infer_datetime_format=True)\n",
    "    data['longitude'] = data['longitude'].astype(float)\n",
    "    data['loadingOrder'] = data['loadingOrder'].astype(str)\n",
    "    data['latitude'] = data['latitude'].astype(float)\n",
    "    data['speed'] = data['speed'].astype(float)\n",
    "    data['direction'] = data['direction'].astype(float)\n",
    "\n",
    "    return data\n",
    "gpsdf = get_data(gpsdf, mode='train')\n",
    "#test_data = get_data(test_data, mode='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished1...\n",
      "12148 12148 12148\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(12148, (42972416, 7))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#预处理特征，得到标签,同时得到起点到终点的距离\n",
    "#因为寻咯数据是完整轨迹，那么为了和测试数据保持一致，需要为训练数据选取一个截断点cut，只保留截断点之前的数据\n",
    "import random\n",
    "from geopy.distance import geodesic\n",
    "def cut_data(df):\n",
    "    cut_list=[]\n",
    "    reserve_list=[]\n",
    "    #labels=[]\n",
    "    df.sort_values(['loadingOrder', 'timestamp'], inplace=True)\n",
    "    sx=[]\n",
    "    sy=[]\n",
    "    ex=[]\n",
    "    ey=[]\n",
    "    dis=[]\n",
    "    grouped=df.groupby('loadingOrder')\n",
    "    for name,group in grouped:\n",
    "        group=group.reset_index(drop=True)\n",
    "        s_x=group['longitude'][:1].reset_index(drop=True)[0]\n",
    "        s_y=group['latitude'][:1].reset_index(drop=True)[0]\n",
    "        e_x=group['longitude'][-1:].reset_index(drop=True)[0]\n",
    "        e_y=group['latitude'][-1:].reset_index(drop=True)[0]\n",
    "        sx.append(s_x)\n",
    "        sy.append(s_y)\n",
    "        ex.append(e_x)\n",
    "        ey.append(e_y)\n",
    "        dis.append(geodesic((s_y,s_x), (e_y,e_x)).km)\n",
    "    group_df = grouped['timestamp']#.agg(('mmax','max'))#.reset_index()\n",
    "    print('finished1...')\n",
    "    #\n",
    "    for name,group in group_df:\n",
    "        reser=int(len(group)*random.choice([0.3,0.4,0.5,0.6,0.7,0.8,0.9]))+1#随机保留一部分轨迹\n",
    "        reserve=list(group.index[0:reser])\n",
    "        reserve_list+=reserve\n",
    "        #label=(df.loc[group.index[-1],'timestamp']-df.loc[group.index[reser-1],'timestamp']).total_seconds()\n",
    "        #labels.append(label)\n",
    "    group_df = df.groupby('loadingOrder')['timestamp'].agg([('mmax','max'),('mmin','min')]).reset_index()\n",
    "    #读取数据的最大值-最小值，即确认时间间隔为label\n",
    "    labels=list((group_df['mmax'] - group_df['mmin']).dt.total_seconds())\n",
    "    return labels,reserve_list,sx,sy,ex,ey,dis\n",
    "#下面只需要保留原始数据cut之前的数据\n",
    "labels,reserve_list,sx,sy,ex,ey,dis=cut_data(gpsdf)\n",
    "print(len(gpsdf.loc[reserve_list].loadingOrder.unique()),len(labels),len(gpsdf.loadingOrder.unique()))\n",
    "gpsdf=gpsdf.loc[reserve_list].reset_index(drop=True)\n",
    "len(gpsdf.loadingOrder.unique()),gpsdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished1...\n",
      "finished2...\n",
      "finished3...\n",
      "finished4...\n",
      "finished5...\n",
      "花费时间: 85.83543395996094\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from geopy.distance import geodesic\n",
    "\n",
    "def get_train_feature(df):\n",
    "    start_time=time.time()\n",
    "    df.sort_values(['loadingOrder', 'timestamp'], inplace=True)\n",
    "\n",
    "    df['lat_diff']=df.groupby('loadingOrder')['latitude'].diff(1)\n",
    "    df['lon_diff']=df.groupby('loadingOrder')['longitude'].diff(1)\n",
    "    df['k']=df['lat_diff']/df['lon_diff']\n",
    "    print('finished1...')\n",
    "    df['speed_diff'] = df.groupby('loadingOrder')['speed'].diff(1)#速度变化量\n",
    "    df['diff_minutes'] = df.groupby('loadingOrder')['timestamp'].diff(1).dt.total_seconds() // 60#记录之间的间隔时间(单位:s)\n",
    "    df['a']=df['speed_diff']/df['diff_minutes']\n",
    "    print('finished2...')\n",
    "    #同时计算时间间隔，也就是运单抵达\"终点\"需要的时间,作为标签\n",
    "    group_df = df.groupby('loadingOrder')['timestamp'].agg([('mmax','max'),('count','count'), ('mmin','min')]).reset_index()\n",
    "\n",
    "    k=df.groupby('loadingOrder')['k'].agg([('k_max','max'), ('k_mean','mean'), ('k_min','min'),('k_median','median')])\n",
    "    a=df.groupby('loadingOrder')['a'].agg([('a_max','max'), ('a_mean','mean'), ('a_min','min'),('a_median','median')])\n",
    "    group_df = group_df.merge(k, on='loadingOrder', how='left')\n",
    "    group_df = group_df.merge(a, on='loadingOrder', how='left')\n",
    "    #以经纬,度,速度，方向为基础特征，然后计算最小，最大，均值，终值等统计特性（以运单为单位）\n",
    "    print('finished3...')\n",
    "    #在所有的记录中，多少记录是停船状态\n",
    "    #df['anchor'] = ((df['lat_diff'] <= 0.03) & (df['lon_diff'] <= 0.03)& (df['speed_diff'] <= 0.3) & (df['diff_minutes'] <= 10)).astype(int)\n",
    "    df['anchor'] = ((df['lat_diff'].abs() <= 0.03) & (df['lon_diff'].abs() <= 0.03)& (df['speed_diff'] <= 0.3) & (df['diff_minutes'] >= 10)).astype(int)\n",
    "\n",
    "    anchor_df = df.groupby('loadingOrder')['anchor'].agg('sum').reset_index()\n",
    "    anchor_df.columns = ['loadingOrder', 'anchor_cnt']\n",
    "    group_df = group_df.merge(anchor_df, on='loadingOrder', how='left')\n",
    "    #merge回原表，增加一列: 'anchor_cnt'，同时算一下停船的次数占总记录的比例\n",
    "    group_df['anchor_ratio'] = group_df['anchor_cnt'] / group_df['count']\n",
    "    print('finished4...')\n",
    "    agg_function = ['min', 'max', 'mean', 'median']\n",
    "    agg_col = ['latitude', 'longitude', 'speed', 'direction']\n",
    "    group = df.groupby('loadingOrder')[agg_col].agg(agg_function).reset_index()\n",
    "    print('finished5...')\n",
    "    group.columns = ['loadingOrder'] + ['{}_{}'.format(i, j) for i in agg_col for j in agg_function]\n",
    "    group_df = group_df.merge(group, on='loadingOrder', how='left')\n",
    "    end_time=time.time()\n",
    "    print(\"花费时间:\",end_time-start_time)\n",
    "    return group_df\n",
    "#   \n",
    "train = get_train_feature(gpsdf)\n",
    "#\n",
    "train['start_x']=sx\n",
    "train['start_y']=sy\n",
    "train['end_x']=ex\n",
    "train['end_y']=ey\n",
    "train['dis']=dis\n",
    "train['label']=np.array(labels)/3600"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 观察训练数据标签的分布\n",
    "- 最长时间:86.28858796296296 day\n",
    "- 最短时间:0.0 day\n",
    "- 平均时间:16.70094809971483 day\n",
    "\n",
    "## 而测试数据中已知的轨迹长度为：\n",
    "- 最长时间:16.88171296296296 day\n",
    "- 最短时间:0.046527777777777786 day\n",
    "- 平均时间:4.325498415081749 day\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最长时间:80.36274305555555 day\n",
      "最短时间:0.0 day\n",
      "平均时间:17.472454240105062 day\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_label=np.array(train['label']/24)\n",
    "##观察下预测结果的分布\n",
    "name_cnt={}#{订单号：对应的事件记录条数}\n",
    "tmp=[]\n",
    "print(\"最长时间:%s day\"%(np.max(train_label)))\n",
    "print(\"最短时间:%s day\"%np.min(train_label))\n",
    "print(\"平均时间:%s day\"%np.mean(train_label))\n",
    "#画出分布直方图\n",
    "import pylab as plt \n",
    "bins = np.linspace(int(min(train_label)),int(max(train_label)),int(max(train_label)))\n",
    "plt.hist(train_label,bins)\n",
    "plt.xlabel('ETA of orders ')\n",
    "plt.ylabel('Number of orders')\n",
    "plt.title('train lables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12137, 36)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=train[train['label']>0.001*24]#筛选训练样本，0.05天以内的不予考虑\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 测试运单数据\n",
    "- loadingOrder 运单的运单号\n",
    "- timestamp 运单当前所处位置（经度、维度）的时间，格式为：yyyy-MM-dd'T'HH:mm:ss.SSSZ，如2019-09-05T16:33:17.000Z\n",
    "- longitude 运单承运船舶的当前经度：114.234567 \n",
    "- latitude 运单承运船舶的当前纬度：21.234567\n",
    "- speed 货物在运输过程中，当前船舶的瞬时速度，部分数据未提供的可自行计算。\n",
    "- direction 当前船舶的行驶方向，正北是0度，31480代表西北方向314.80度，900代表正东偏南9度。\n",
    "- carrierName 承运商名称，类似快递公司名称\n",
    "- vesselMMSI 脱敏后的船舶海上移动业务识别码MMSI， 唯一标识，对应到每一艘船\n",
    "- onboardDate 离开起运港时间，格式为：yyyy/MM/dd HH:mm:ss（dd与HH之间为两个空格），如2019/09/05 16:33:17\n",
    "- TRANSPORT_TRACE 船的路由，由“-”连接组成，例如CNSHK-MYPKG-MYTPP。由承运商预先录入，实际小概率存在不按此路由行驶（如遇塞港时），但最终会到达目的港口。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loadingOrder</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>speed</th>\n",
       "      <th>direction</th>\n",
       "      <th>carrierName</th>\n",
       "      <th>vesselMMSI</th>\n",
       "      <th>onboardDate</th>\n",
       "      <th>TRANSPORT_TRACE</th>\n",
       "      <th>temp_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>QM149151037282</td>\n",
       "      <td>2019-01-11 04:18:28+00:00</td>\n",
       "      <td>114.337167</td>\n",
       "      <td>22.556333</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10680.0</td>\n",
       "      <td>UQCRKD</td>\n",
       "      <td>Q4197827697</td>\n",
       "      <td>2019-01-11 04:18:28</td>\n",
       "      <td>CNYTN-NZAKL</td>\n",
       "      <td>2019-01-11T04:18:28.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QM149151037282</td>\n",
       "      <td>2019-01-11 04:39:08+00:00</td>\n",
       "      <td>114.426632</td>\n",
       "      <td>22.508475</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13140.0</td>\n",
       "      <td>UQCRKD</td>\n",
       "      <td>Q4197827697</td>\n",
       "      <td>2019-01-11 04:18:28</td>\n",
       "      <td>CNYTN-NZAKL</td>\n",
       "      <td>2019-01-11T04:39:08.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>QM149151037282</td>\n",
       "      <td>2019-01-11 06:05:48+00:00</td>\n",
       "      <td>114.418000</td>\n",
       "      <td>22.182000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>22210.0</td>\n",
       "      <td>UQCRKD</td>\n",
       "      <td>Q4197827697</td>\n",
       "      <td>2019-01-11 04:18:28</td>\n",
       "      <td>CNYTN-NZAKL</td>\n",
       "      <td>2019-01-11T06:05:48.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QM149151037282</td>\n",
       "      <td>2019-01-11 08:48:28+00:00</td>\n",
       "      <td>114.101650</td>\n",
       "      <td>22.314968</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4420.0</td>\n",
       "      <td>UQCRKD</td>\n",
       "      <td>Q4197827697</td>\n",
       "      <td>2019-01-11 04:18:28</td>\n",
       "      <td>CNYTN-NZAKL</td>\n",
       "      <td>2019-01-11T08:48:28.000Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QM149151037282</td>\n",
       "      <td>2019-01-11 09:50:58+00:00</td>\n",
       "      <td>114.123500</td>\n",
       "      <td>22.324167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7310.0</td>\n",
       "      <td>UQCRKD</td>\n",
       "      <td>Q4197827697</td>\n",
       "      <td>2019-01-11 04:18:28</td>\n",
       "      <td>CNYTN-NZAKL</td>\n",
       "      <td>2019-01-11T09:50:58.000Z</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     loadingOrder                 timestamp   longitude   latitude  speed  \\\n",
       "0  QM149151037282 2019-01-11 04:18:28+00:00  114.337167  22.556333   28.0   \n",
       "1  QM149151037282 2019-01-11 04:39:08+00:00  114.426632  22.508475   30.0   \n",
       "2  QM149151037282 2019-01-11 06:05:48+00:00  114.418000  22.182000   27.0   \n",
       "3  QM149151037282 2019-01-11 08:48:28+00:00  114.101650  22.314968   15.0   \n",
       "4  QM149151037282 2019-01-11 09:50:58+00:00  114.123500  22.324167    0.0   \n",
       "\n",
       "   direction carrierName   vesselMMSI         onboardDate TRANSPORT_TRACE  \\\n",
       "0    10680.0      UQCRKD  Q4197827697 2019-01-11 04:18:28     CNYTN-NZAKL   \n",
       "1    13140.0      UQCRKD  Q4197827697 2019-01-11 04:18:28     CNYTN-NZAKL   \n",
       "2    22210.0      UQCRKD  Q4197827697 2019-01-11 04:18:28     CNYTN-NZAKL   \n",
       "3     4420.0      UQCRKD  Q4197827697 2019-01-11 04:18:28     CNYTN-NZAKL   \n",
       "4     7310.0      UQCRKD  Q4197827697 2019-01-11 04:18:28     CNYTN-NZAKL   \n",
       "\n",
       "             temp_timestamp  \n",
       "0  2019-01-11T04:18:28.000Z  \n",
       "1  2019-01-11T04:39:08.000Z  \n",
       "2  2019-01-11T06:05:48.000Z  \n",
       "3  2019-01-11T08:48:28.000Z  \n",
       "4  2019-01-11T09:50:58.000Z  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_pathB='../R2 ATest 0711.csv'\n",
    "test_data=pd.read_csv(test_data_pathB)\n",
    "test_data=get_data(test_data, mode='test')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_name_xy(name):#输入港口名称\n",
    "    port_name=port[port['TRANS_NODE_NAME'].isin([name])].reset_index()\n",
    "    return port_name['LONGITUDE'][0],port_name['LATITUDE'][0]#返回港口经纬度\n",
    "#  \n",
    "start_x=[]#起点\n",
    "start_y=[]#起点\n",
    "end_x=[]#终点\n",
    "end_y=[]#终点\n",
    "#存储中间结果,避免重复计算\n",
    "temp_dic={}\n",
    "for value in test_data['TRANSPORT_TRACE']:\n",
    "    s_e=value.split('-')\n",
    "    start_port=s_e[0]\n",
    "    end_port=s_e[-1]\n",
    "    if start_port in temp_dic:\n",
    "        re=temp_dic[start_port]\n",
    "    else:\n",
    "        re=convert_name_xy(value.split('-')[0])\n",
    "        temp_dic[start_port]=re\n",
    "    start_x.append(re[0])\n",
    "    start_y.append(re[1])\n",
    "    if end_port in temp_dic:\n",
    "        re=temp_dic[end_port]\n",
    "    else:\n",
    "        re=convert_name_xy(value.split('-')[-1])\n",
    "        temp_dic[end_port]=re\n",
    "    end_x.append(re[0])\n",
    "    end_y.append(re[1])\n",
    "test_data['start_x']=start_x\n",
    "test_data['start_y']=start_y\n",
    "test_data['end_x']=end_x\n",
    "test_data['end_y']=end_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#temp_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "finished1...\n",
      "finished3...\n",
      "finished4...\n",
      "花费时间: 0.6123332977294922\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from geopy.distance import geodesic\n",
    "def get_test_feature(df):\n",
    "    start_time=time.time()\n",
    "    df.sort_values(['loadingOrder', 'timestamp'], inplace=True)\n",
    "    # 特征只选择经纬度、速度\\方向\n",
    "    dis=[]\n",
    "    #df['dis'] = df.apply(lambda x: geodesic((x['start_y'],x['start_x']), (x['end_y'],x['end_x'])).km, axis=1)\n",
    "    #for i in ranngedf['start_x']\n",
    "    grouped=df.groupby('loadingOrder')\n",
    "    dis=[]\n",
    "    start_x=[]\n",
    "    start_y=[]\n",
    "    end_x=[]\n",
    "    end_y=[]\n",
    "    for _,group in grouped:\n",
    "        tmp=group[['start_x','start_y','end_x','end_y']][:1].reset_index(drop=True)\n",
    "        start_x.append(tmp['start_x'][0])\n",
    "        start_y.append(tmp['start_y'][0])\n",
    "        end_x.append(tmp['end_x'][0])\n",
    "        end_y.append(tmp['end_y'][0])\n",
    "        dis.append(geodesic((tmp['start_y'][0],tmp['start_x'][0]),(tmp['end_y'][0],tmp['end_x'][0])).km)\n",
    "    df['lat_diff']=df.groupby('loadingOrder')['latitude'].diff(1)\n",
    "    df['lon_diff']=df.groupby('loadingOrder')['longitude'].diff(1)\n",
    "    df['k']=df['lat_diff']/df['lon_diff']\n",
    "    print('finished1...')\n",
    "    df['speed_diff'] = df.groupby('loadingOrder')['speed'].diff(1)#速度变化量\n",
    "    df['diff_minutes'] = df.groupby('loadingOrder')['timestamp'].diff(1).dt.total_seconds() // 60#记录之间的间隔时间(单位:s)\n",
    "    df['a']=df['speed_diff']/df['diff_minutes']\n",
    "    #\n",
    "    group_df = df.groupby('loadingOrder')['timestamp'].agg([('mmax','max'),('count','count'), ('mmin','min')]).reset_index()\n",
    "    #这里的label表示测试集中给了多少天的轨迹数据\n",
    "    group_df['label']=(group_df['mmax'] - group_df['mmin']).dt.total_seconds()/3600\n",
    "    k=df.groupby('loadingOrder')['k'].agg([('k_max','max'), ('k_mean','mean'), ('k_min','min'),('k_median','median')])\n",
    "    a=df.groupby('loadingOrder')['a'].agg([('a_max','max'), ('a_mean','mean'), ('a_min','min'),('a_median','median')])\n",
    "    group_df = group_df.merge(k, on='loadingOrder', how='left')\n",
    "    group_df = group_df.merge(a, on='loadingOrder', how='left')\n",
    "    #在所有的记录中，多少记录是停船状态\n",
    "    df['anchor'] = ((df['lat_diff'].abs() <= 0.03) & (df['lon_diff'].abs() <= 0.03)& (df['speed_diff'] <= 0.3) & (df['diff_minutes'] >= 10)).astype(int)\n",
    "    anchor_df = df.groupby('loadingOrder')['anchor'].agg('sum').reset_index()\n",
    "    anchor_df.columns = ['loadingOrder', 'anchor_cnt']\n",
    "    group_df = group_df.merge(anchor_df, on='loadingOrder', how='left')\n",
    "    #merge回原表，增加一列: 'anchor_cnt'，同时算一下停船的次数占总记录的比例\n",
    "    group_df['anchor_ratio'] = group_df['anchor_cnt'] / group_df['count']\n",
    "    print('finished3...')\n",
    "    #以经纬,度,速度，方向为基础特征，然后计算最小，最大，均值，终值等统计特性（以运单为单位）\n",
    "    agg_function = ['min', 'max', 'mean', 'median']\n",
    "    agg_col = ['latitude', 'longitude', 'speed', 'direction']\n",
    "    group = df.groupby('loadingOrder')[agg_col].agg(agg_function).reset_index()\n",
    "    print('finished4...')\n",
    "    group.columns = ['loadingOrder'] + ['{}_{}'.format(i, j) for i in agg_col for j in agg_function]\n",
    "    group_df = group_df.merge(group, on='loadingOrder', how='left')\n",
    "    group_df['start_x']=start_x\n",
    "    group_df['start_y']=start_y\n",
    "    group_df['end_x']=end_x\n",
    "    group_df['end_y']=end_y\n",
    "    group_df['dis']=dis\n",
    "    end_time=time.time()\n",
    "    print(\"花费时间:\",end_time-start_time)\n",
    "    return group_df\n",
    "test=get_test_feature(test_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 观察下测试集中给了多少天的轨迹数据\n",
    "- 最长时间:16.88171296296296 day\n",
    "- 最短时间:0.046527777777777786 day\n",
    "- 平均时间:4.325498415081749 day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最长时间:26.122685185185187 day\n",
      "最短时间:0.004050925925925926 day\n",
      "平均时间:2.5039671063757822 day\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察下测试集中给了多少天的轨迹数据\n",
    "test_label=np.array(test['label']/24)\n",
    "##观察下预测结果的分布\n",
    "name_cnt={}#{订单号：对应的事件记录条数}\n",
    "tmp=[]\n",
    "print(\"最长时间:%s day\"%(np.max(test_label)))\n",
    "print(\"最短时间:%s day\"%np.min(test_label))\n",
    "print(\"平均时间:%s day\"%np.mean(test_label))\n",
    "#画出分布直方图\n",
    "import pylab as plt \n",
    "bins = np.linspace(int(min(test_label)),int(max(test_label)),int(max(test_label)))\n",
    "plt.hist(train_label,bins)\n",
    "plt.xlabel('ETA of orders ')\n",
    "plt.ylabel('Number of orders')\n",
    "plt.title('test lables')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12137, 36), (219, 35))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.drop('label',axis=1,inplace=True)\n",
    "#train.to_csv('train.csv', index=False)\n",
    "#test.to_csv('test.csv', index=False)\n",
    "#train=pd.read_csv('train.csv')\n",
    "#test=pd.read_csv('test.csv')\n",
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [c for c in train.columns if c not in ['loadingOrder', 'label', 'mmin', 'mmax','k_min','a_min','direction_min','anchor_cnt']]\n",
    "#features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train['label']=np.log1p(train['label'])\n",
    "#train[['count','dis','direction_median','direction_mean','direction_max']]=np.log1p(train[['count','dis','direction_median','direction_mean','direction_max']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test[['count','dis','direction_median','direction_mean','direction_max']]=np.log1p(test[['count','dis','direction_median','direction_mean','direction_max']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 搭建训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## xgb+lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error,explained_variance_score\n",
    "from sklearn.model_selection import KFold\n",
    "from matplotlib import pyplot as plt\n",
    "#from featexp import get_univariate_plots#用于特征筛选，需要先安装featexp\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((12137, 36), (219, 35))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape,test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12137, 28)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data=train[features]\n",
    "y=train['label']\n",
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(train_data,y,test_size=0.1,random_state=2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_mse: 6.447885e+03\n",
      "train_mse: 4.791750e+02\n"
     ]
    }
   ],
   "source": [
    "llf=gbm = lgb.LGBMRegressor(objective='regression',\n",
    "                        num_leaves=31,\n",
    "                        learning_rate=0.1,\n",
    "                        n_estimators=600)\n",
    "\n",
    "llf.fit(x_train,y_train)\n",
    "# 网格搜索，参数优化\n",
    "'''\n",
    "estimator = lgb.LGBMRegressor(num_leaves=31)\n",
    "param_grid = {\n",
    "    'learning_rate': np.linspace(0.1,0.5,3),\n",
    "    'n_estimators': [100*i for i in range(1,10)],\n",
    "}\n",
    "llf = GridSearchCV(estimator, param_grid)\n",
    "llf.fit(x_train, y_train)\n",
    "print('Best parameters found by grid search are:', llf.best_params_)\n",
    "'''\n",
    "\n",
    "llf_val_mse=mean_squared_error(y_test,llf.predict(x_test))\n",
    "llf_train_mse=mean_squared_error(y_train,llf.predict(x_train))\n",
    "print('val_mse:','{:e}'.format(llf_val_mse))\n",
    "print('train_mse:','{:e}'.format(llf_train_mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_mse: 6.375277e+03\n",
      "train_mse: 4.154470e+02\n"
     ]
    }
   ],
   "source": [
    "cv_params = {'n_estimators': [400, 500, 600, 700, 800]}\n",
    "other_params = {'learning_rate': 0.05, 'n_estimators': 500, 'max_depth': 7, 'min_child_weight': 1, 'seed': 0,\n",
    "                'subsample': 0.8, 'colsample_bytree': 0.8, 'gamma': 0, 'reg_alpha': 0, 'reg_lambda': 1}\n",
    "\n",
    "xlf = xgb.XGBRegressor(**other_params)\n",
    "xlf.fit(x_train,y_train)\n",
    "xlf_val_mse=mean_squared_error(y_test,xlf.predict(x_test))\n",
    "xlf_train_mse=mean_squared_error(y_train,xlf.predict(x_train))\n",
    "print('val_mse:','{:e}'.format(xlf_val_mse))\n",
    "print('train_mse:','{:e}'.format(xlf_train_mse))\n",
    "#optimized_GBM = GridSearchCV(estimator=model, param_grid=cv_params, scoring='r2', cv=5, verbose=1, n_jobs=4)\n",
    "#optimized_GBM.fit(train_data, y)\n",
    "#evalute_result = optimized_GBM.grid_scores_\n",
    "#print('每轮迭代运行结果:{0}'.format(evalute_result))\n",
    "#print('参数的最佳取值：{0}'.format(optimized_GBM.best_params_))\n",
    "#print('最佳模型得分:{0}'.format(optimized_GBM.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第1次验证mse误差为5284.674640\n",
      "第2次验证mse误差为3954.280932\n",
      "第3次验证mse误差为5367.293285\n",
      "第4次验证mse误差为4233.425893\n",
      "第5次验证mse误差为4737.864688\n"
     ]
    }
   ],
   "source": [
    "details=[]\n",
    "answers=np.zeros(len(test))\n",
    "n_splits=5\n",
    "cnt=0\n",
    "sk=KFold(n_splits=n_splits,shuffle=True,random_state=2020)\n",
    "for train_k,test_k in sk.split(train_data,y):\n",
    "    cnt+=1\n",
    "    x_train=train_data.iloc[train_k]\n",
    "    y_train=y.iloc[train_k]\n",
    "    x_test=train_data.iloc[test_k]\n",
    "    y_test=y.iloc[test_k]\n",
    "    \n",
    "    xlf.fit(x_train,y_train)\n",
    "    pred_xgb=xlf.predict(x_test)\n",
    "    weight_xgb=mean_squared_error(y_test,pred_xgb)\n",
    "    \n",
    "    llf.fit(x_train,y_train)\n",
    "    pred_llf=llf.predict(x_test)\n",
    "    weight_lgb=mean_squared_error(y_test,pred_llf)\n",
    "    \n",
    "    \n",
    "    prob_xgb=xlf.predict(x_test)\n",
    "    prob_lgb=llf.predict(x_test)\n",
    "    \n",
    "    errors=[]\n",
    "    ij=[]\n",
    "    weight=np.arange(0,1.05,0.1)\n",
    "    for i,item in enumerate(weight):\n",
    "        prob_end=prob_xgb*item+prob_lgb*(1-item)\n",
    "        error=mean_squared_error(y_test,prob_end)\n",
    "        errors.append(error)\n",
    "        ij.append((item,1-item))\n",
    "    \n",
    "    ii=ij[np.argmin(errors)][0]\n",
    "    jj=ij[np.argmin(errors)][1]\n",
    "    \n",
    "    details.append(min(errors))\n",
    "    details.append(weight_xgb)\n",
    "    details.append(weight_lgb)\n",
    "    details.append(ii)\n",
    "    details.append(jj)\n",
    "\n",
    "    print(\"第%d次验证mse误差为%f\" %(cnt,min(errors)))\n",
    "   \n",
    "    test_xgb=xlf.predict(test[features])\n",
    "    test_lgb=llf.predict(test[features])\n",
    "    ans=test_xgb*ii+test_lgb*jj\n",
    "    \n",
    "    answers+=ans/n_splits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 观察训练/验证过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test_end_score</th>\n",
       "      <th>xgboost</th>\n",
       "      <th>lightgbm</th>\n",
       "      <th>weight_xgboost</th>\n",
       "      <th>weight_lightgbm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5284.674640</td>\n",
       "      <td>5300.963384</td>\n",
       "      <td>5699.082993</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3954.280932</td>\n",
       "      <td>4048.380414</td>\n",
       "      <td>4266.207881</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5367.293285</td>\n",
       "      <td>5385.005052</td>\n",
       "      <td>5839.539134</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4233.425893</td>\n",
       "      <td>4243.589545</td>\n",
       "      <td>4806.185427</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4737.864688</td>\n",
       "      <td>4890.239576</td>\n",
       "      <td>4955.264014</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   test_end_score      xgboost     lightgbm  weight_xgboost  weight_lightgbm\n",
       "0     5284.674640  5300.963384  5699.082993             0.8              0.2\n",
       "1     3954.280932  4048.380414  4266.207881             0.6              0.4\n",
       "2     5367.293285  5385.005052  5839.539134             0.8              0.2\n",
       "3     4233.425893  4243.589545  4806.185427             0.9              0.1\n",
       "4     4737.864688  4890.239576  4955.264014             0.5              0.5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#观察训练/验证过程\n",
    "df=pd.DataFrame(np.array(details).reshape(int(len(details)/5),5)\n",
    "                ,columns=['test_end_score','xgboost','lightgbm','weight_xgboost','weight_lightgbm',])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 观察预测结果的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测的最长时间:38.94155130118619 day\n",
      "预测的最短时间:1.6265700219252468 day\n",
      "预测的平均时间:19.27223800510642 day\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##观察下预测结果的分布\n",
    "re_day=answers/24\n",
    "name_cnt={}#{订单号：对应的事件记录条数}\n",
    "tmp=[]\n",
    "print(\"预测的最长时间:%s day\"%(np.max(re_day)))\n",
    "print(\"预测的最短时间:%s day\"%np.min(re_day))\n",
    "print(\"预测的平均时间:%s day\"%np.mean(re_day))\n",
    "#画出分布直方图\n",
    "import pylab as plt \n",
    "bins = np.linspace(int(min(re_day)),int(max(re_day)),int(max(re_day)))\n",
    "plt.hist(re_day,bins)\n",
    "plt.xlabel('ETA of orders ')\n",
    "plt.ylabel('Number of orders')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测的最长时间:41.53280218409299 day\n",
      "预测的最短时间:0.7503500481643698 day\n",
      "预测的平均时间:15.602533636239563 day\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.close(fig=None)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub11025=np.load('../submit/11025.npy')\n",
    "##观察下预测结果的分布\n",
    "re_day=sub11025/24\n",
    "name_cnt={}#{订单号：对应的事件记录条数}\n",
    "tmp=[]\n",
    "print(\"预测的最长时间:%s day\"%(np.max(re_day)))\n",
    "print(\"预测的最短时间:%s day\"%np.min(re_day))\n",
    "print(\"预测的平均时间:%s day\"%np.mean(re_day))\n",
    "#画出分布直方图\n",
    "import pylab as plt \n",
    "bins = np.linspace(int(min(re_day)),int(max(re_day)),int(max(re_day)))\n",
    "plt.hist(re_day,bins)\n",
    "plt.xlabel('ETA of orders ')\n",
    "plt.ylabel('Number of orders')\n",
    "plt.show()\n",
    "plt.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "answer与sub11025的mse:44041\n"
     ]
    }
   ],
   "source": [
    "print(\"answer与sub11025的mse:%.0f\"%mean_squared_error(answers,sub11025))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征重要度筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,9))\n",
    "lgb.plot_importance(llf,max_num_features=29)\n",
    "plt.title('lightgbm——feature_importances_',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1296x648 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,9))\n",
    "xgb.plot_importance(xlf,max_num_features=29,importance_type='gain')#'gain'可以换成weight\n",
    "plt.title('xgboost——feature_importances_',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'xgboost&lightgbm——feature_importances_')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tvfxfjI/jeHeGAHdWx3/IMHLg7PjynhmuX679kOdVdeWq+sXxEU9vS/KoDMHmrO1nZ7hR8joZhsp+66xoho4tfzv+n63Ku8e6nFRV+15Pefjc9N4DlL/++O+nD/A6u9QU/wADC2qtXWb8mXwv3PH5o29L8r3ZOIj7zSQ/nY0DuGsmeX1V/UFVHX6g9+rES7NxcnX5VVdmyd6ZoW03XnVFtkNVHVFVp2U4ibhKhrZ+JclPZrhT77PjvKOSPKuqXllVx6yqvpv04gzfryOT/MGK67IdnpnhIswtq+rWq67MNnhfhm3uTuPzkzatqq6XjWdY99q7YHZX9RUvdanpuH2SO2T4TGY/ybBPueqqKrWgKd/UM+XtctaDc6qjM1xCVR1dVS9I8rM5cG+JdbnpbsrbZTLNbfNZGfaVV8swnPckVdWeDDc/3j8bF3b/V4Zw7KQModnsu/eDGS5uP2jna3pIpnx8OfntsqqOqqrnJHlZkitl2P6+lOQBrbVHt9aelOFm3U+MRS6f5I+r6nVVdexKKr05U//snjv+e8equsZKa7INJrqvPKAxsH5MVb0ryb8m+bkk35ONtp+b5ElJrtta+/HW2otaa6dkGEXs8f+XvfMMl6Uo1/b9EERyEpUMKqCIJAUBQYmioogfHkkiOYgiIOjBDCh6MCuKYiCDW5BwDqKCEpWcBYmKgASRnPN+vh9v955es9fMmpk1M93Tq+7rmqtnurt6vbWquruq3kTcswCvAb4yZPHHoODLjE3H8QKwv+1NbW9PrBk9kR17FaHA/KmkqkbEGpesru/JjA3uJdaQVqTRbs8SjgLr2V7J9g9s32P7GNvrEWnx8nDLsxFeuWVxeradhWiPDSS9RdJPiHvOwCW2728umOU+Xjk759bm44kpju30SZ/0SZ8p8yEUs38ilLbTs8/twJqFc5YDriwcf5mwplqxbPnb1Gsu4KZM1q+XLU+f67ZB1g4vAsuVLU+f67YycGNTf/wLsEzhnIWAMwp98WUi3NpGZcs/Qd0WIyaMLxMD7leXLVOf63dw1ia3Aq8pW54+1+1Thf52GTBfh+XmJixh87KfLLsuLeT8aibjP8uWZcD1nA34FpHLMn/GPEmEeCo+T44H5i1b3g7r9Kv8OVm2LAOoW237JeHlkPe/BcqWZ8B1XYvILVd8r5+d3WfF++4/wOZly9tBfWrbL7P61bJvEjniniAWe3cpW54+121WIlz5i4X77Glgz3HO3QJ4sGlOd1yV33k1H1/WuV+uQYTKLz77zwUWH+fc+YhUF83vhA+UXY+p2HZZ/f43a48zgVnKlqdPdar1s3KcOmxIGJI/U7iv8vo8D/yG8FbVBNdZJuvr04G7S6zP0kS43WI9/gas0ubc4jMlj/xSettMUM9lgUMI45bmdptOeMp/Epi/g2vNQSjppwMPlVin2WiszTZ/8jbarEXZ9xbO+XzZ7ZM+1frIrl24+0QikRgXSVsQIWQWomFteByhaHiq6dzZiIWrz9Cw/noO+G/bhw9N6C6Q9EZCQb0o8HXgqx6dEJltkXQk4SV9OaG8HPn8EJL2JSZWr6DhDX4ocIgjn2Xz+XsQFolzZudPB34AHGj7xebzyySzGIXIW/JJoo4vEArqG4DHGZvztyW2DxmEjP1A0leALwMPE233G9v3livV5JE0N2HcknsF3EFYJp/Zom8K2IxQGC6f7X4AeEMV71VJixOT4HmBj9r+Vcki9R1JKxAKz1VovO+uBrYlFiaOJRYy8vvwLmB72xdTYSRtQCyKvkwYVt0+QZGRoc79UtKshBfBWsAxtkclNUDHZM/BLxFRXWal4S3xOdvfy87ZFvgxMH9WzMS4dD/bzw5d6A6oc7+EevZNSe/Mvr4F+DYxBrsBOIXux2AXDULGySDpCuCtNN5t1xM5psfN2Zx51h1HhMfM630n8c67ZLDS9kYdx5dToF++QOPZ/yLRft90mwVXSdsDh9OIdGDgZ7Y/PmBxu6LubQcgaT4ibPSGxDthP9vXlSvV5Jgiz8olgB2BnQjlK4xND/cPwtP4aNv/6eK6JwLbAC/ZLiUin6THiLFXXp8jgU+3Gi9moc6/QIxFxzyLbB82eIk7pylH8frMnN/2GSKiwc9sX97ltX+eXXe67dn6InAPSFqeWJsdLyT3dx1pV8YrdxKwNXEPrmv70vHOS0xNkhI3kUi0RdI8RK6B+YHZOy1XxQG6pOk0wto9Tlgh/nqCMu8iBrN5mDUDZ9t+3yBl7RZJH8u+Lk5MGl9BWFOeSfeTq+MGIeNkyMKKHEkM0m8FDgTOsv1SmXJNhqb+eBew3UQTpEwxcxIRhoWs/A22Vx2krN1SqNuY3ePsmxDbs/ZFqD4i6Y7Cz8WI+y2v2+N0fr/ZdiXzL2U5mk+nkTfIxGLi5UR/fZrwvF0KWJNGWF4Rnp9b2P7dkMXumMyo5xQiNNOWtv9Yskh9YxyDDxMK9i8Wn5mS9iMMfoqGJN8ADrb98rDl7pQ6GvXk1LxfLgb8AXgz8R7bv5sFtSojaWki19o6NBagbiYWS69vce47aLwnbiPGAJXMk1vnfgn165vjjMF6Gn8RY5TSFkBb0VS/3JhxQqNVSQcAXyPms6LExflW1Hl8OUX6JYQR5La2r+6w3LLEO2HtbJerNveZAm13VPZ1NuC/iPsOwoi1m3UUV8kQqM7PSgBJvwM2oZEmMh9/vUB4Vv/M9rk9XvtbwP6UeD8WnikPE97v/9dhubcTHsl5SqQqPlMeoWHQWFS6X0cYN55g+8ker1162xVkmYdQKK9LGOvcDfza9p9anL8wERJ6diJCzCrtDIESU4+kxE0kEjORWSJ+irA+W4GxL9ZOqOoAPR8I/YXwJri73fmFcvMTuWD+K9tV+oCgmRaTK+h+glW5tpN0XuHn2kSYFBOe0bfS3cRqo/5L2BuF/vgr4OO2n+iw3GyE4uXTxKSlqv2xH1SubtBSSQ3d3XeiovXLkbQVMZGap7C7Xb0BngJ2tX3yIGWbDJKWyr5uTHjFzQ78lt48Cjp6jwwLSWcAH8h/EjnMPmb7vBbnr0woLVbMdplIJ7Ct7TvGK1M2dTTqgdr3y1pHZ6i5t0Rt+yXUs29OkTHYv4EdbZ/TZdlViXfeG6lg/eo8vpwi/fKXwD7dGpdl74SvEDkcZ6la/aZI29XO+LjOz0qYySAewiDuF0RUjYcmee3SFYFZ/f5ItN9MuVMnKDsPMWbbngq2X1PbPQVMI5TuV/Xh2nsCWwHY3mCy10skqkRS4iYSiTFIWp3w3nxtvquHy1RuoADkYY4OBr7hcUKCdlB+J8KKce6q1a/Ok6s2EyvG2d/yMlSsbpIeB/ayfWKP5TckvMQXrVK9gDwMXF+wfXC/rtUv6ny/NSPpdcTC0kcIA4pWPE+EPTrY9j+HIVuv1NmjoGlSfAahUH9kgjJzEJ67exXKPml7/nblyqCuRj0wZfrlmN3j7JuQKj4za+4tUdt+CfXsm5IuoLc2mokqLoBKOhPYqddFekmvBL4H7F6VNsup8/hyCvTLD9v+zSSvsQ5wfAW9qC+g3m1Xy/uuzs9KmNFuzwOnEQrAC0sWqa9IOsD2tyd5jY8AP7W9UJ/E6gtZ211NOMqcZPvpkkVKJEaCpMRNJBIzyMI33AIsXNj9EpFL4mHCS6AjKjpAX8v2ZZO8xuuJ8B5rT3jyEJF0dL+uZXunfl2rH9R4YrXsZJVdkhYCjrT9XxOenOgbWTjMvmD7rn5da5BkEQnWIUJ5L0J45z5FhG2/FrjE9uPlSdg5TYrO8SIYdEqlnikwo27PELm8ft5l2fcBRwGvpoJ1g/oa9cCU6Jf9oHJ1gynlLVGrfgn175uJ1kh6v+3fli1Hkak4vkyMRdK87jGMaKI3JO3Qr2vZPrZf16oKVXxWAnlamGMnMlad6khawvY9ZctRRNKqHvGc04lEGSQlbiKRmIGkrxNhCU3E4P88cJztp0oVrGJImtUVzhdYNxR5iftC3Sw0E4lE90i6k/55FCzbj+v0C0lXA9vYvq3H8q8GjgbeU0WFRJ0VLjXvl3WPzlBnb4k7qWm/hPr3zUQikUgkBoGk7YCNqFgu4H4xBep3EPAxKphDfbLUuW6JqU1S4iYSiRlIug5YGZgOrG/7LyWLlEgkEolEokMkzW6746gZba7zSds/6odM/SQZ9STqTBW9JRKJRCKRSCSakXQ48AkqaBjZD1L9Rpdh1E3SO4u/bV/U6thkKF43kahcXppEIlEqyxLW9hcnBW4ikUgkEqNFPxS42XUqp8CFpHhN1JukwE0kEolEIpFIJCrPBTQi1Zix+rXiscnQfN3EFCd1hkQiUWSWbHtLqVIkEolEIpFIJBKJRCIxAVlu6VWAVwHz0pjTtsX2cYOUKzF1kbQCsDGwKt31S9veaJCyJRKJRKIvqMdjiURPJCVuIpEo8i9gBWDOsgUZBpI2pffJVcqtkOgbkuYCtmfm/tjJ4M+2K/0+lyRgRbq/17B9yABFm7JI+ljxd3Ehs/nYZEgLpIlEIpFI9BdJSxV/27671bHJULxuFZG0LbA3sAbdL5gaSGOUkpG0ON3PDSobXlLS64CfErk0uy5On3KQD4O6tV0ikUh0wUW0fl63O5ZI9EylF30TicTQOQd4I/C2sgUZJJLWAo4BlivuzrYeZx+FYyMzuUpW6aOBpP8iJvsL5LtKFKevSJod+AywF7Boj5epvBJ3RA1CjmFsCKDjWhybDGmBtCKkhbZEYnhImh9Yl97eCbsMUrZEbbiT1mH8iscmQ2XD+EmaEzgZeF++q83pnuB4ZRnR8eWESFqHUL5vBCzcZfEq98tViTCaHRvi5kUHJVO/qWvbFUnv8EQiMRG21+/lWCIxGSr/Ak0kEkPlCGAPYAVJMQ8utAAAIABJREFUm9j+Y9kC9RtJGwNnEc+/Vkra5n1k+0diglV3q/Q6haeStB2N/3exrVpN6kdmsi9pIeCPRDt1upDRqr6VpAYGISkEUI2ZCgttU4VsQbEbBfzQveeSdz9IWhA4DNgOeGWPlxmZBeBR6JfdMoKRQ6bqe/wXwGbZ9+eA84FlCWPkfC4zL7A0sDIwe7b/aeA0yh9/taUG48txkTQL8EPg4/muEsXpK5nh6mnAfNmu3xNtuA2wBdEmG9Lol+8ENgfmAJ4C9gduG6rQXVDntsuZau/wRCKRSIwWaXEmkUjMwPZtkvYjlLnHS9rI9t/KlqtfSJobOImYyEN4Px4NfALIFxiXZezk6mPAa4jJ1V7An4coclfU3Sq9buGpJC1M1EfAi8AXicn+l4k+aduzZB7VeX/cnfCufgrY3fa0EkTvlJOB1bLv/wZOJ7z81yDa4hAa99o7gNfSWGD7MbEoV1lqYBBybI/HRgJJLxd+jgk53nRsMlQylPlUWGjLqZNRTxFJSwN7EnV7C41xSyeUpYA/hins3Z+12UXAEtTXcGkU+2VHjGDkkLtp3V/aHRt5JL2dUIwZ+Dvwbtt3STqcUOJie6fC+fMBuxHj63mAVwNb2X5y2LJ3Qg3Gl+34NnGP5dwMzA8sRtTjIuI9vhTxTofG3ODq4YnZEx8FliHkPd72jgCS3pmfYPvCwvk/lrQY8DNi7v4t4D22LxuWwF1S57abEu/wRCKRSIw2lZ1IJRKJcrD9U0nPEsqlKyUdCfwGuMH2E+VKN2l2ISYVBr5t+78BJM2YxNu+K/t6I3CWpIOA7xIeykcBW9o+c5hCd0FtrdJrGp5qd2BuQtbP2/4OQDiBNLD9FPA34G+Sfgp8FvgGcKKkeW3/fKhSd4CkdxPW5gauAjax/US2wLYGgO2DC+fPAnyIuNeWADYFNrN9/7Bl74Q6GIQUFzi7OTZC5IYb4z0D2h2rA7VeaIP6GfUUkXQA8DUaz5dR6qdT0isw8948HVgy2/VX4ETg3UQfNbAzY98Ja2TnPgUcDDw0RJG7ZsT7ZVtGMXKI7WV6OVYTdih837kwdxuXbP76HUn/R8yNNiXGbB8enIi9UYfxZSskvQnYh7hXHgQ+YPvKbG7wCQDbGzSdvxdhODI3cCuwt+0Xhy17h7w/274EHNBJAdv3SdqcmIdvDkyTtIrtxwckY0/Uve2mwjs8kUgkEqNPUuImEokZjOOdJOBT2Wcm5VIbKumdREzaIRScX+2kgO1ngY9LmhXYFThG0ptt/3tAMvZEna3SaxyeauNs+wThNTchtg0cli3yfBH4vqTzbP9jQDL2ytaF77tMZABiezpwqqSLgAsJb+NpkjbIjlWNuhuE1IUpp1Cq+0Ib1NaoBwBJnyFC+eU8Rcg/b7a9O/u+IGNDaz4H/Gd4ko5Lrb37J2BLQgFo4Gxgc9svSVqKzNDA9pj/gaS3Eh5YqxH37Ltt3zJUqTtkxPtlJ9Q6ckgNeUe2/YftizstZPt2STsQCvsPSdrM9lkDkbB36jy+3I2GEdUutq9sd7Ltm4G9JU0jPJN3A6Yz1kitSqxO1O0K2x0r9GxPl7QH8B5CibgDHc4Lh0jd267W7/BEItF/sufDwBmFVCSJ4VFFJUsikSiPoneSaZ9zZxRZmajTZZl340xIUqYoa2Z/Qmm4ALAT4QlZJWprlU59w1PlHtKXtVKYSJrV9nihX79GKGQWICyDvzAwKXtj7Wz7N9s3dlrI9oOSPkp4765L9MeTByDfZKmtQUhdsN0ypG67YzWg1gttNTbqQdKSxLMdQtZdgFOBH9BQwC+bnTsv8Yzci4jAMTtwpO3/GbLYM5gC3v3t+FC2NfBx2y9NVMD21ZLWJRRK6wAnS1rTdqUUgqPeLyei7pFDasriRHtd27R/xvxN0itsv9Bc0Pa5kv5G5D3+KPHeqxJ1Hl+ul23v7UZ5bvtiSbsD04A9JJ1k+y8DkXBy5JFN/t60f8Y8TtKcWXuNwfYDmSHrxsTcp2pK3Lq3XW3f4YlEYmDcyeCjsFQ6FUli+NR5ESuRSPSGCtvipw4snG3/2bS/OFCfc7yCmYfqBcT/You+SzZ5erZKJxTAIrNKH4Rwk6Sn8FTEwv3/EYv90yTNPxjxemahbHtP0/6iQrdVf3yBRn98b98lmzy5l8oNTfuLC2xzjFfQ9jXEQirAdgORbvJ0ZBDSouz+hPdObhBSOSR9Ofu8p8fyG+fX6LdsiQnpeaGNCPEuYqFt3UEI1weKRj3H2d7M9inAvfkJti+0/VvbP7a9FfB64HdE1IlvAc83Gf5UhT1opDn4pO1TWkUisP2k7d/b/gCNKByHpnuuNNYk2uCaiYzoimSL+TsSi/xvBrYdiHSTo+79suvIIbZPJTx1b6UROSStqwyP3Ijn4ab9ReXYvG3KX0O8697aT6H6RJ3Hl0sTdWs2LivODcbNs237ZBrz9x0HIVwfeEW2faZpfzHK1SJtyt+ZbV/XL4H6SN3brs7v8EQiMVia1837/UkkZpAmG4lEYga2Z+nTZ9ay69KC/CXYbJldnFy9tk353Mp+KKEzuqQjq/TxCto+l8i5CrFAXjV6Dk9FLD6+QCM8VZXIF0GbvXCLC4iLtSmfL14t0TeJ+sfc2faxpv1PF74v0Kb8jcT9+pZ+CtVH6mwQAnAQ8BXCk70XNi1cIzFc6r7QVlejHoA8zPVDwPGdFrL9a+DTxDPlS5JWGYBsifa8Otve3LR/hrJT0ivHK2j778AlRPttPd45JVP3ftlz5BBizCwakUMSwyFXkjW/y4pjzqXblM/ng4v2TaL+UefxZT7ubw6x/nzh+zxtyl9K1O0dbc4pk0ez7dxN+x8sfF++Tfn8PfKqNueURd3brs7v8EQiMRjuzj53TfB5kbEeuybGK/dm2+ZjLxTKplDKiTEkJW4ikZhKPJJtmycZxXBTb2pTfvFsu2DfJOofdbZK7yg81XgFbT8AXETUrWoLbLlCer6m/UXP3JXalM8XqNq1a1nkhhHNE95HC9+XbVM+L9fOqKJM6mwQkhht6r7QVlejHgiPYQOXt0jrgKRWIbWOIJ4rsxAh9hPDJX9nNXvOFX8vRGtuz7Yr9E2i/lH3fln3yCEzkDS3pEUlLdXpp2yZW/CvbLtw0/5iqPy1ac2K/RWnr9R5fJmHmW029n688H3JNuXz/0k7A9cyuZ1ov2bj2uKzZZPxCmYKwjWyn22jAZRE3duuzu/wRCIxAGwvY3vZVh/CaOd0GtFsjifeAQvYXsj2krYXIubumwAnZuflqYOWy9OVJBI5SYmbSCSmErcSk6tm6+zrC9/H9TzLvHbenv18dLxzSqbOVul1DU91C/F/b5brusL3cS3pJS1K5N+BsRbeVSEPRdXcLkUL53bhWnOPnZnymVWEOhuE9IP8eTLoPDGJman7QltdjXqg8Txozq1ZVMDPNV7BTLn2Z6JuG/ZftMQE5Ivuze1TNKx7fZvyuWf4q9ucUxZ175e1jRwiaVZJ20s6S9J/iH56D+Hl2cnnjlIEn5gbiP95s8LkChrjjl2zHLFjyHIg58ZAVaxfnceX92Xb5vvpH4Xvb2tTPvdirWp+wGuy7Zub9l9GY866u6Tlxin7VWIOPl5UrSpQ97ar8zs8kUiUw4+AfYi1uvVs72D73CxqxgyyVCTn2t4eeCfx3NkX+MnQJU5UnqTETSQSU4k8vGTz5OpywitSwE6SxnggZXmufkRYYDo7v2rU2Sq9ruGpLsu2b25aaLqaWGQTsI2kMaGZJM0LHEP8Pwz8ZfCids1fCfmbF5ouo6Fs2VPSTAu/krbLypkwvKgidTYI6Qf5Ik+z4UVi8NR9oa2uRj3QUKA3Gz8UvXIWpzVPdXBOJZA0i6QVJW0maRtJH+v0U7bsLcjvr2YPub8Vvq8/XsFsjLl69rOKz8y698taRg7JFEXXE+PF9xBj4LrkYrso264gaYZ3nO1/EWNiEbllT5e0qqTZJc2fPT9OKlznzKFJ3Dl1Hl/+jahbsxLzqsL3caNkSFqDmMea6oaXPC/bvlrSjHl1ljf1eKLu8wFXSvq+pN0k7SPpPCL0fM6xQ5O4c+rednV+hycSiSEjaWNgt+znNrYv7aSc7UuAbYjn7c6SNh2QiIkRJSlxE4nEVOLcbLugpBlhg22/BByZ/ZwDOF/SGZIOlfQ9wmNy28J1fjYUabujzlbpdQ1P9cdsOw8Nr9rcc+V72c9ZgBMlXSfpREmnE16uGxeu86NhCNslF2Tb12Vew8CMHHJ/INpzWeAiSVtIWk7SKpK+DPy8cJ3ThiVwl9TZIGRSZPlW189+3luiKB0haT1JP5Z0haQHJT0v6eUOPy9N/BeGTt0X2upq1AONENjN+XrvLHxfrU35XDE9ridyFZC0rKRfEu14A5Gn+ATg6A4/R5UgdidcR9x3zQZxl9HwWP24pPH63b7E+9CEV2fVqHu/rF3kEEkLAxcSBnFFZezTxHt5ovxtVc/F9jvifhGNPOk5B9KY92xGGEY+R3i4Hk3DQ/Uh4PsDl7R76jy+/HO2XVHSDE9j27cT3qcC1pV0RNaHAZD0TmAajX58zpDk7ZaziXtMxCJ8kS8RBtci0uDsDfwU+C7wrsJ559g+iepR97ar8zs8kUgMn12z7XW2z2t7ZhO2z6URkWGXvkqVGHmqamWfSCQSg+A8YhK/ELA9MbHPOZRYCFiFCEP5gezTzHG2fzdgOXvhImBrMqt0249AWKVL+guwHg2r9C8TC/1zAR8kJpA5VbRKv4bIz9gqPNU8RHiqX2STySJVDk91MeE1txihVPlz4dgPCc+JTQjZ38LYUH35ZPjrmcVe1TiL8LidBfgQkRcv578JJfQriIXfU1tc4w7gxwOUcTKcC3yGzCDE9tUQBiGSjgS+QMMg5Hc07rfNGBuOq3SDkAk8297Yhefb7ISn1RbEor1peJtXDkmLEMqjjZsPlSBOP/kzcc+tKGke209BLLRJupa459aVdATwJdsPw4yFtqOJ+pvqLrTdTihiJzLq+VNzwYob9QDcRCwEvqFp/zWF71sx1pMMAEnLE+9J0/DGrhSStiS8keZg9O+zZi4AdgeWkPQ623cA2H5c0qmEIeBrgKsz48DiGKz4jJ02VKk7o9b9kogcsiqtI4fMQkQOOcL2GC+rCkcO+RyNXL9PAt8Aptm+s0yh+oXtuyV9l3gPLNJ07FJJuxEKslZrXQ8CH+wmr/oQqc34chz+QMw5ZwM2Zez4/8s05qB7ALtlIcDnZKwByTOMnbdWBtvPSdqIMBR4tOnYo5LWI56T64xXHDgO+PjABe2NWrcd9X6HD5LbCYOhulL3+v2OMA6qI2XXbQ3iuX7dRCe24Hpizv7WiU5MTC0UDj+JRCIBkl6e+KyOsO1KGolIWoLw4HnW9t1NxxYglE1bMfMC4zPAt4FDbE8fhqzdIGkpIn8VwE62jyscW5uJQ+6KWNR4c9UWNSR9EDidTJlp+6bCsR8Be9FYqDqGsROrd9FQSmxfNetmSbMRRgPTbb/YdOwVwEHAJ5k5N9a9hALmmCGI2ROS9iYU1Lc2y5mFhpnGzJ49ObcBHxhHKV8Jsnb7N2EQ8kPb+xaOvRK4lDAIaTXIEnCs7Z0GLetESJrOzHJONqetiMXvdW1XzhtEEcb7asJjczLKJNueKbpBmUh6I6F0MfAR26cWjm1GLLTl7Tqd8LIrLrSJ8CRZyXbuoVYZJP2A8GB5wPaihf1zEvfkPISCds3m54ekbwH7E/X/k+1KhaiSdCDwdcJrbEHbzxeO3UL0VwOfB76Vj0UkLQucQiOixtG2d6VCSFqJuOdmL+y+m/BaeRTo2Ku9Cs/NZiTNR9xLswOfs/3NwrHFCUVhu7yqIv4/6zSPBcqmzv0SQNKOhIe3gSVs3184diahHMsNAb/G2DHmgUQ4ZQOft33YUIVvgaRbiWgMLxN52CprUDUosnfhp4lczIsR77s7gN8C38siw1SOOo0vx0PSycCSwHm2v9B07CBCIZiTe1vnPEuEpfy/Qcs5SLJ5+UaM7Zdn2a6SIchM1Lnt6voOV4TqBjjd9uE9lN8T+Agx39mor8L1gbrXLzG6SHqGMLg60XbXqWAkHUs4HT1ne6b0Y4mpS1LiJhKJGRQW8yfrIVG5he1ukLQYM0/6z7P9WKmCTUC2QL0EcJXt7zQd25nOrNIrt9CTLVo8SCjfD7X9pcKxBQkLtyVpv6Bxtu33DlrWQZApc9dkbH+81iP+As9CUu3GzAsZvwWOsV2Z0ITjUReDkOy532+eAfa1/YsBXHvSZNEIDiKeGS8TC/jTyBRKtvtl0FQKNV9oq7NRz+pE2GsD77N9duHYDoSndP7cf5Tw/JsLWInwFhTwIrC67WIet9KRdDywHSH/TcBuVRxvTAZJHyK8xO+1/dumY3nUiWVaFL8Q2Mr2f1ocL40690uYEZXhPkLWvW0fUTi2IrEw/4oWxSHq9w9gtTzyQdkUFg/Psz1uupFEdanL+LIXJG1CeCK/k8Z99wThVfVV2ze3Kpsol1Fvuzq+wwtrez+2/akeys8wfqzi2l7d65cYXSQ9QKTuud32G3sonxvjPWT71ROdn5g6JCVuIpGYwSQW8/MHyQzvrTQQqh6japUOIGlNsvBUtq9oOrYU7cNTHQ983PazAxc0kSgwCgYhko4eZ/cONMJDduJJa8JL62EirO3vbT/ZNyH7jKS/EgoGA1vaPqNkkYbKKC+01d2oR9IVhPyn2v5k07GjgB0Lu5rHXtOBvWxXLoympH8R4dafBJa3/UDJIg2dzCBrS8YxXLJd1fDlQH37ZU7dIodI+jcRZvjntvcsW55E/xmF8eVkUOT4fRVRt4dH3XB1KlHXthvFd3jdlZx1qF/Bm3iQlOJpXOe6TYSkc4ENiP65ne2OQ61L2pZI+WTgQtsbDkbKxCiSlLiJRKJrJIlYzHgLYQm8KzGQ3dOFML6JxDAZ1fBUiUTVmOykuOpIeooIIXyp7XXLlqcsRnWhbSob9UjanVhwWq6w24SxxRdtD2PBpGskPUsYDJxhe8uy5Un0l1Htl50yapFDJF0MrAX8xvZWZcuTSCQSieHTByXnd4D9gJdst4tIUQp1qF+LtEZ9/ROUpKSuc90mQtKuRG763NB9l04UuZK2AX5BrFMY2N32Lwcpa2K0qGTOykQiUW2yhd7HgD8Df5b0S+Bs4GhJc9o+slQBE1MS25cSuaISicTkuJuYODxctiAD4jliclS50J7DJAu3WKnQb53QrLhtOnY3sG5djXoyb8afZeE287r903bV79UHCE/Oykb8SPTOCPfLjrD9EPCN7DMKTAPWBtaTNOuopwhIJBKJRCksmm0rkSpgAFSlfp2msmuOdNLrOcOkznVrx1HAnsDqxJrDiVnUl+MJA8e7iRQIcwFLEYZ3H822eeqfa4mUJYnEDJISN5FITBrb12Z5sc4Cvi/pQtu3lC1XIpGoFpJWIhbyFwRmS5771cT2MmXLMGD+SfTBecoWJDEY6m7UY/se4J6y5eiCm4hn/xJlC5IYHCPYL+vK0cCBwGuBA4DDyhVnOEiaG5gPmL3TMs05ZxOJfiHpNcBbgWXpvl8eMii5+o2kOWnM69L9VBMyo6z3EMqkO0oWp+9UqH4bdHDO24BDiYg2TwH/C1wG/At4mkgxsyShAPwgMb99HvgicFX/Re6YOtetLbanS9ocOA9YPtu9VvbphH8AHxzV/PaJwZHCKScSib4h6SZgBeBHtvcpW56JkLQGsAbdT65se5eBCdZHJM1H5KHLJ1cXlSxSYhyyCfD76b0/Vi4XSI6kpYHPAtvQlFOuOfxNtuDxQ8IC8Srb3xyWnImpg6QvAF8F7rT9urLlSSTqjqQPAacSCzJL2X60ZJESiVojaS3gHMLL40vAN+vmkStpVmBbYGti/Lxwl5ew7Uo7NWR54N8DrEvDCHLW5nF/lupozuzni7ZfHKqgiRlIWocYY76LHj3GqhgeNCfrax8GtiP65YLZoZnupywU/Ueyn7fb/uPQBJ1iZPnpm9mRUFLeQnj/dcLsxNrR22mEdP2O7c/2QcyeqXv9WiHpg8CvCbmPBA60/USb8+cjDLf2AF4AtrL9v8OQtVvqXLccSQsC3wM+1kWx44H9bD8yGKkSo0xS4iYSib4h6TgiDMSttt9UtjytyHINfJVQlvVExSdX8xLhO7YDVqIxgRxvcvVqwkof4Abbxw9N0EQ+ET4w+/TiFVjZXCAw4147krCibF7IGFduSecSlpvPAK+1XUqII0ljBttFr+HmY5MheSMPH0kLExau8xI5ao4pV6JEot5k+ZcvBtYEfm1725JFGihZfd9Ibx5Y6Z2Q6AuS3gQcR4Tz+zdwJuEV/zgd5qmran+UtBxwOpDPN3tRllV2/Awg6QDCCLKonB533J+Na+4ilBKX2x4vL/zAkVTMfz3GyLTp2GSorPGqpH2B7+Q/e7xMZfulpBUIpctb8l2Fw+P1SxEKtjcQz6Aly/Isk7RU8XfRa7j52GQoyxu5Rf7RGWtAvV6WeF+sbPtfvcrWD+pev/GQtCRwAzFf/abtz3VR9n+I98cTwCq27xqMlL1R57qNh6TlgV2INa6VCc/jnBeAvwLnA0eNeuqfxGBJStxEItE3JP0M2BV42va8ZcszHpK+A+yb/+zxMlWeXL0LOJFGjo+2k6uszFXAakSe40VtvzBwQWeW4Z3F30WP4eZjk6FqnsiSTga2ZHK5PSrZHyVtCZyc/yT616XA64mwMq364zZEHzZhYfmb4Ug8kxzFyeIYA4gWE8leqLwXSBFJiwOvIiZcs3RSpmr3XI6kDwC/IXI37mz7VyWL1HckrUfDQylXJnXa30amb0palZm9lEYiWsZUQtIiwJ+Ixd+zgQNs1yovtaRliRBvH6Y3w6zK33dZFJvcAyu/52YZx0hwASBXJN1j+69DFTSR98ev0PAA6XbcUsn+mCksbwBew9jx89PEWPOlTq9lu2eD3kEhaXbgDMIDFzo3gvwRsBfRzivY/vtABR2Hwvh4JmVzn8bOlTVelbQucCGN9noJuAS4EXiU7vrlwX0XcJJkRiEXE1GV8jo+Q7Tp3LTul/sQnmgGNrHdL2V+V9R9XpfVod9cDHzK9rUDuHZX1L1+4yHpa8DngQeBxW13/AzJ3iP3EPP2w2x/fjBS9kad69YJkuYn5glP2X68bHkSo0NS4iYSib4h6SJiUedJ2/NPdP6wyfISnFHY9TCRx7eXydWx/ZVu8mSTxz8Sll2iEV5mAUKp22pytSvws+z8zW2fNTShGzLUemI1Hlke6aNpLHbcDJxCb/3xwkHI2CvZ4u0dRN+bDnwN+Ibt5yUdDnyC1v1xbuARQtn0M9sfH57kY+QoThbHW4jqB5VciCqShYXbG9iImoUqlLQBcCwRVusaItxrt15KlVNSZ8qyE4CNmw91cZlR6JsfBg6i4YkFrb2UXkO08ezAX2z/v2HJ2SRHMRyci8rmFqHiemHMdatEljrgMOCTxD12O93dc1Wu25ZECLQ5qKeR4CLEmOW9xd3Zdrx7bnYi//iiwD9tv2Eogo6DpGIY4eYxZr9CDFfqfSfpPcQ77ZXF3V1eppL9UdK3gU8Tz4yngG8A02zfWaZc/ULSL4Cds5/PEeOU84jQ0R+k9fh5LUJpaCIU4w+HI/EYGabs2FnSacAWxP//AmAHR57wkScLXX4jkTYLwmPs87Yv72BetxiR3xLgW7YPHIbM48jRiYHBZIyqab7uMMnWFcbsAo4i6nUuYSA9ESaeOQ8TEdr+01chJ0Hd6zcekq4nour9xvZWPZSfRoQzv8H2Kv2WbzLUuW6JxCCpzEQjkUiMNpLeAbyDGBz9s2RxWrFXtjXwK2AP20+XKE/fyHImTSMWDyHCp33O9v2FyVUrTgN+SgyGNyYU22XQbuI02UlVFdm58P2bRHvVxbJqd0KBa+Bg21/ttKDtpxX5tVfJPmXRzlCjckYc/SYLB/pDIFei1/EevIp4F3yGCDe5epflTcXG0pLmAi4ivN3r2GYASPopsFv+c6LzbT+QhWr/KLC5pMVt3ztIGVuwI2OVlbu0OTYZKqnoJBQQm2bfBSyXfbqhcnWTtBJwEmPDJt9ND0ZZVSRbhL+E8Lzt6Lli+0VJPyFLXyJpLduXDVDMduSGjePJ3u7YSJL1xzMYG67vLuBv1KA/Ah/IttOBTUvsV31H0luBnYg+eS/wbtu3ZMfaRiayfZmkxwhPyfWIMdyw2aDHY3Vg7Wz7MLCF7SfLFKbPbEcocE2EMf+IOwyLbPs+SXcArwPeNjgRJ+RuWo+x2h0bCcZzMCgYB95SRQeEbqh7/VqwZLbt1VMzzy+7ZNuzyqHOdUskBkalFp4SicTokS0Ybwf8D42FkDNLFao1byXkuw/YyfaLJcvTT3YBFiPq91Pb7ZS2Y7D9iKRbifxt3Sox+sVFtJ48tTs2yryFqNffy7JKHiDvy7YPEV5X3XIbocB9Xd8k6hLbO/VyrEZ8m4bhC4Sn+Pw0njMXEWGVlyLCGZHtfxq4enhi9oak1YA/ELLnz5c6LOIfQGOh7SXCSn0amTLJdr+8zkojC8G1e2HX2YSX0ntov0h8LKHEFfGM+vmgZJyAdjm8+tEHK/m+lHQI8IXsZ64069ozsK9C9Y//JhS4JjyLd6uTYokIPZ/nDLwZOIS4577C2PdEM9MIJS6E8r7M/8lUMhQ8kFDg5v1xV9uXlytSX1mSzNuxZvcZhAI3n09vnytwu+Ba4j34polOHATtIgNVLWrQAMiNV8+tmQIX4EPZ9jng450qcAvcSKTT6dZoq2/YXqaXYyNOrtis23Myp+71yw0DX99j+XwdpYp6nzrXLZEYGKnDJxKJGUjqJkfJbERozTcw9lnyEPDdfsrVR+bKtufXTIELDav0J4nFxG65mZjwlxLuzvb6vRwbcfLB619KlWIw5Eqkv7i3HMuPZtvKhWVnWyjvAAAgAElEQVSfCmR5r/Yh2vBB4AO2ryx69dveoOn8vYA9ibxYtwJ7V/U5K2kpQgFR7F/PA/9g9L2UPlz4/hHbZ7Q8cwSRtBzw2eznY8CWts/Pji1FeyXu+UTozbmB9SlHidvOAKS2xiGS3k/kis3pOZ1FRVk/2z4FbGz7gRJl6SuStgDWInunA++1/Ux2rK1S3fY/JN1DhKxfa9CytpGjZQ73dsdGmPw5+DSRg/LfZQozAJ4AFiHe2XUjb7sbe1R65hEmFu+TPInOuRdYloaHWJ3IDeH/YvvBHso/nG27TcuSmBx5rteFJc1e1XnZJKh7/e4CVgTWk/Q623d0WlDS64F3Efft3QOSbzLUuW6JxMBIStxEIlFkfbr3cihar99HLPY/2urkkvkXYQH6fNmCDIDcq/Mi20/1UP6RbLtA/0RKTMBdhOK8bh4gAAtl215zzeQhAPuVPyvRHbvR8ATZxfaV7U62fTOwd5af5qys/HTae2iVyRcIBa6B+4H9gTNs1+Hd8DqiXpfWTYGbsTsxfzHhXXZ+pwVtT89yML0DePOA5JtIhpbh3moaCi5n72xrwjtz97qks8jIPfr/VCcFbsZHsu1LRI7HZ7osfz2wBBHiPTEciv2xbgpcCOXtIsCCZQsyAPJoJ9dOdGIL8ufq3P0RJ9EF1xFjsNKiCA2QRbLtnT2Wz+dzaf15uHyfeJ5cY/vwsoUZAHWv31mEonNW4NeS3t3JOqukhYCTs3KmvFRp7ahz3RKJgVFHy9NEIjE51OXHwC3Al4EVbfc66RwGVxAyv7FsQQZAbtnaa46/XJGY3gvD48/E//0tZQsyAPL8JvP2WD4P2/hw27MSg2K9bHuv7Y4nR7YvJpRsAvaQtO4ghOsD7862LwAb2v51TRS4EKHuIHIf1pGNsu0/bJ/WQ/m7su0SfZIn0Rlr0DCa2KlmClyAXHHbi4dS1cm9cC+1fWcP5fP/ySJtz0r0k/x/3qshXdWZRowz1pM0a9nC9JlXZtvn2p7VmvmybS8GvYnJ8ZNsu66kunlC52Pk2due1ZrXZNuqGvrXlXysdWOpUgyOutfvhzQ8+1cHbpS0q6Rx11ckzStpN+CvwKrZ7qcoJz/6RNS5bonEwEiWUIlEoki7MITNvEiE7r1zhPK+HE7kw1tL0gq2by1boD7yNOFFO9dEJ7Ygn2wmpdnw+AHhsbi6pLfXLF/ZvYQnyKoTndhMlmc7XzS+rc9yJTpjaeL/3+yBOyNSQ6uwVbZPlvQNYBlgR6oZLjz3dDnPdt362D8J76R5yhZkQOR9s9fnZb5g0KuBSaI35sy25/cYYr/q3ETk6ayjccCrs22vz8q8vefogyyJzrgJWJT6htQ9msj7+1oiD/xh5YrTVx4k2m2xHsuvVLhOYojYPlfSb4H3A8dKel+N3ncPEOOmFbotKEnA24mx27/6LFeiPfcTOUe7jbQ3KtS6frbvlbQ7cCLhaLEocCTwY0m3EKGEnyHW/5YiHFVyHY+Al4nIN706eQyMOtctkRgkSYmbSCRm0GPunZEhy+n4C2BXYJqkjWw/MlG5EeF+YuG+6xCRkmYH1iYGwP/ss1yJFti+RdJXga8Q/XET238vW64+cT6wCvAmSat16aH/SWLAbiJvaSlIeucw/o7ti4bxd7okD6ve7MVT9Fadh9YW9ZcSecHe0We5+sUjhGKijotJZxC5y9YuW5ABkYeI7NXLKFdu9+rllOiN+wjDjrr+338OvAd4l6QFK5xWpBfyMJi9ejy+Kts+1gdZEp1xArAxsH4N+yO2n5K0JXAOcKikWYBv2n65ZNH6wS2EMchakmaz3XG+cEkrEErc8YzwhoKko4bwZ2x7lyH8nV7YGjiNiPhypaTPAX+wPerpYS4lUlK9TdKitu/vouz/I8bcBqo456kzVwFvINI31ZG61y83jn4O+AUxnhLhEb8SDaOdnGKKroeB3aqcWqfOdUskBkVS4iYSianGnsQgYBfgBkmHAqd3ORmpIn8m8kqsKun1tv/RRdkdaOSHvGAAsk2IpKUmPmvy2L57GH+nU2wfLOkl4GDgWkk/ISb/N9l+on3pSnMSsG/2/UhJ63eSR0/SRsAh2c+XiIXIsriAwVv2mmqOxZ4jlGXNi/aPF74vSWslbu550KsnyaC5nVhQqmN4z58CnwGWlrSj7WNKlqffPEx4X716ohNbkKdTSF5Kw+VywrCjay+eEeF/iTquCfwY2LZccfrKg8T7YJkey+ehtO/rl0CJCTmBmOesR7wTtipXnP5j+zJJbweOA74GfFLSmYQX8uN0OH6zfdzgpOyJPxAK+IWBnQgDkU75No1UR2f3X7SO2JHheMVVUolr+xlJ7wW+BBwEnAk8K+k2Ou+Xtr3RxKcNlTOAjxFzlm8C23dSSNJijA13ekr/ResMScMw8rDtKs3rjicMC9aQtKLtm8oWqM/UvX4A2P4/SW8C9ieesa9lrFKzyANEtIrv2n5oOBL2Tp3rlkgMgiq9YBKJRGLgZJawu0m6mZjsHg4cLulxuptcvX6AYvbCKcAexKDnx1kIpwmtfiWtRPwfIOr+q8GJ2JY7maIKM9uHSnoa+C4xgN0fIKJPdXqJSk0YsX2VpNMI6+u3AhdJ2tv2peOdn03y9wX2ISwwDfyiAkr3jhuhZtxHWNwv0LS/aBzyNiIvzXgsn20r1S8LTAPWJbyUXmm7Nt6Bth+WtD3wG+Ankp63XdZzfRD8gwi59fZuC2Y56lYjni/X91muTmX48jD+ju1DJj5rqPyMWGhbW9Ibbd9StkD9xPZ0SZsDfwK2lrQgcIDtOuSmvopQwK8laQHbHXvUZoZZSxL33MUDkq8TOe4Ywp+pzNwg648fJowCPyzpHODTtuuWN/A5Iv/7W4n3wm5dljehBK4SxxAKwPmAb0u6yXbbe0fSK4AjgM1oGExMG7CcbUXq4lxPcP54x6seOvW/iflMLvtcRHSiTsiV8JXC9hmSrifqsW02b/10OwNdSe8jjEgWJep0bsmpg/L/7ZSZ29n+vaQzgC2AEyVtWKfIDHWvXxHbDwOfl/QFwvN4VcIYeR4iOtGDwHXAzbYr9wxpR53rlkj0G6V7IJFITCWy0MGHE9bNszF2IN/JA1HEQk2vYeUGhqTzgXcR9fg9sKfteyQdDnyCgtzZ/2E34OvEQoGBU2xvXZLs0xn8xKpy7SZpfuDXwCbF3V1epnL1ApC0AHAJ4fmW31v3EvVbPNt3BqEszMOA53W/BniH7WL43qEi6QI6eyasTSiec9kfJsL0Pk14Ly1JeFSQXe8F4LK8sO1ucpEPBUmnAh8Crre9WmH/csCtRD3+Yvtd45Rdg0b9brNduRBXkuYEriCiF/zI9j4li9R3JG0AHEvca9cAp9K9h1Llwt5J+iLhrW/gI7ZPLRyb6V3XVPZoIvKEgU/Y/ulwpB4jQ/6uGygVfSfk7XMDsGG2aFMrsmfLYURaABNe/93cd5ULEyrpo4Siy8D3bB9QONbynpM0L/GcXSEru6ntPw1N8LGydHLfjTf2Gq/MeAqlSs0NCiFtZwf+K9sC/J1Qeo5sf8yR9B7ivfbK4u4uL1OZNisi6RPEXNVE3r+jibnCDoQHpIkISssDmxJRppagoaTayvZvhi85SNqhg9OWBj4PvIKQ9xJi3Ng8dl4LWIeo1/PAN4C7AGwf22/Z+0E2zvhYcVcPl6lqv1yZCIc8b7brceC3RD9ck2jLfYl53aZEmFuI/8GDwNtsl5bGJHsPdEL+bOyk7ZrPrVzbSZqPiM7wfiL91sHAyd0YZFWZutcvkUgkiiQlbiKRaImkuYmQcBsAqxO5CuYFngQeIhaGzwNO6iRUatlImg04iwhTBb0rDCs3QAeQtASxYPaabNd0IofNq2go0r5PTK7eRVi35f+DO4jJVSkDXkl30tmC0mKMVb6bmETmk/75m469SAzoY4e9bH8knjyS5iI8U1ZmcsrrSvZHAEmvIby71892tWrjYv3PI5Qzlc5XnS1QH0MoO3NP6uPGC2Uu6fVEiKB9CYv8M4AdbT85LHm7QdK+RH1eBBa2/VTh2NU0vBmPBL6UK2OyPMJHE15bpsIK0qxNfkcsMh0NHFin0ExZ//wiEVq5l8F+5Tz8YcZ77jZgDiJn8ztt354da6dQ+jIR2tBEGPClbT89RNFzObrNi9eTl1IV3wlZ3sqvE33yP0QI1NNqkM5iBpK2JhYQl8t2dX3vVa3tsrHzLcRzHeALwGG23eqek/RmYlF1FeJ/cLXtNYcreYMu77tOFvDHO6cy910LpXVPHn5VqVORLIrQVYQSMOcuQkH9KJGOoyNs79Rf6fqDpO/R8OYccyjbttp/iO2DBijapJC0DqH4m58wON5nvHFz4fw3AD8A3ku07QdsXzIMWbsli4JyLI338s1EpKwb6b5fXjgIGSdLNs7/NY21hnbPlLxP3g980PZVg5StH0j6FBEu+hVEqqrjaG1gsAMR1ed54LO2Dy9D5nZIOi//CryDWENx9rmTUK4/28Gl7OqF+K59/RKJRKKZpMRNJBLjki3if4Xw0pyxu/C9+PB4AjjI9g+GIVuvSNqbmAjmk6uziYlIL5OruwYh42TJckqcSiPvXydKs78Bm9v+5yBlmwyZ5/BhhBJsOnAiMbG6oqgIyxQXaxIKs22Ien4f+G/bHbfvMJD0JWKxN7e0/yWF/mh7GLl7Bo4iLvRHgf2I8DituJlo4xPcQSjwssnCN32AMIDY1PaE4RozxeE5RG7Bs2xvPlAhe0TSGwnvsfG8HTcjcnzlz5bphEJmTmJRDuK+expYqYrPykJI2/kJBcTshIf0RXTnpVTFsLVIWo3Iq/eq4u4uL1MZhUQzkg4hFNQmjMoOI56dnwb2IpNd0sLAu4n3xttoKDH2tn1ESbJ/pYPT5ifqkXv430PkW21eRHw74X0FsYj4E7K81bYP7qvgk6QpnO3iNMLmQ8g8yuksgBn98gvFXT1cppL3naT1iTFzbthxG6GcWAfYkGi7LWh4YG1E1D9/F6xj+4bhSt1A0tIdnPZBGov3/wROovXi/baEUvt5InTq/0J15gY9GIu0oqr98QSiDUyMVXYtOUzrQJC0M5HyJk9tUXxGNj9fHgP2q6qHKkD2Tv4rkfPweGCnTkNjSjqOmEvcD6xSRaM7SRcRSj0T7XZgHUN/Zga6XyfuwTnanPoSYczzedv/HoZsk0HSQUQo82eBnW2f3EGZrYCjiIgAh9oeSsqMTmkThaIbo55KRZooUvf6JRKJRDNJiZtIJMaQeUqcTHiXdZvT5jRiwb+SDxZJVxJ5kwzsW0WLyX6QhfM7gFBOvLrNqY8RCs7vlOGR1A2SjgR2JTzAt3CL3KpNZdYhPB4XBo6y3W2urIEi6QYijLCBLW2fUbJIA0fSa4nww4sRioqngQeAy2zfWaJoXSFpS2IBezrhwX5dF2VXIzxIoMSQdxMh6WRiwfo8219oOnYQUFyoaPYGfBbYxvb/DVrOXqizl5KkpYh8r/MXdj9P5JLt1lipcqG+YYZhyClEzu1W7fgYY3M65/3zGNs7D1zIHskMKH5HGHpcB+xv+/w2529ELBavQiieNnMF881OsNBGi2PjnVvJhTZJ7weKz7uHicgvvRgJVlIJI+m/iKgFc9F5aOKniHfBWYOUbbJI2p0wgphOKOK/086QTtKsxDj7UKKue9k+chiydkKHIW07oor9UdK9RJ7Np4DlR0FB1CuS5gF2Bt5HjJ/nLRx+nojA9FvgSNtPDF/CzpF0IKH8ewJYvJu5Z/Z/uIeo/5dsf30wUvaOpMeJKFd3EP2ykush/ULSq4joZuPN6y4HzrZ9X3kSdk5TKphtOlHgFspuTRj9TAfWtX3ZBEWGxhQw6Kl1/RKJRKKZpMRNJBJjkPRdwmslX5R/iAiHejERliS3Rl+aCFuyNQ1FoYHv295/uFJ3hqSnCG+xG2y38wisBVkIvLfRenJ1se0XypOwMyRtTHgvGtjE9nkTFCmW3Qj4Y1b2fbbPHoyU3SPpWcLj4zLb7yhbnkTnSDoT2Ay4xPa6PZT/M/H8/J3t9/dbvmEgaRMiLOo7aYQ0fIJQQH3V9s1lyTYRdZ70ZwYvuxHPvPuB/YEzXGJ+6UGQGZwdBHwOyNugnZfSy8DXquahWiRLYXEVkV/uHMJgacJ2kzQH4Qn4biJn9RrFEOhVoM73HICks4nc9gamAbtX3TiuF7IwyT8k0qwUGS+s9wWE1/vfhiBaz2QRbK4lvMP3s/3DLsruA3yPiOSwuu2bBiNlooik5wmv8P+1/f/KlmeYZO+J+YGnbT9etjzdkBlTr06P7SbpNMLj/xrbb+u3fJOlsM5wbJWNxRIzI+mXwE7Arbbf1EP5m4mx23FVCtEu6V39ulYVQ3zXvX5FMieN9wNrEIae89PIdz8RlQ4XXee6JRL9pnK5rhKJRHlkCxl701iM+SERAme8fLdXAKdkVrVfI8IYCthb0s+r6AlCWEgCXFmqFEMiCx98GQ3L0lFl12x7XTcKXADb50q6lgjjuwsRDrAq5ErcSi9wJsblLcRzsldF5S2EEvctfZNoyNj+I/DHTJn2KuL5+vCIeB5UVpHXB96dbV8ANrR9W5nCDIos5PqXs4W3fYl8ecuPc+rdhEfkdzoJeV4yOwErEO+G7TtVvNt+XtLHCEO75QmvrY4VUUOiMvnoB8QaNAwndhoFA7leyBSyG0lamYZn4HhGgr+1PSpj7T2Jsdjd3ShwAWz/QNKnibDmewKfGoB8iZl5kPDE/U/ZggybzDhkVA1Elsm2D/ZYPi/XSXj0MrgXeAM9RHVJlE4eBvuKHstfQYzfKmWUXXXF5GSpe/1yJH2GSCMzTy/FqfAzqc51SyQGQVLiJhKJIjsRHi0Gvmn7wIkK2H4OOCCzis49YnYGPjtIQXvkX0Su2E4tuxLVIF8c7ThkbRPXA6sRobSrxN2EEm+usgVJdM1rsm2vbZeXaxfufCTIlGkjtZBaZW/MPrAY8bw8r64K3CJZDsr9gP0kLUQs7M9QKI1YmM2tibY7v9t8f7b/I+k8QrG2FRVT4lYlV+gAmTPbnl9XBW4R238lclvWgdyD+i89lv8zkRty475JVHGylAofo7wc1TcRz/rFS/jbid7Jx75LtD2rNXm5qs6bLiTygo+sgeYUJn+WtAyjPwF5ucX6IEsiMQNJvyDWaLtJczcS1LluicSgSErcRCJRJF+AeAT4UpdlDyJCOC5MdRcyzgHeRIQYTowOi2bbXpXveejFRdueNXzOBFYmPFkSo8UjRH9ar9uCWT7PvNyj/RSqX0i6Jvv6LLC+7RfLlCfRFY8QxgH/KluQYWP7EaL+o0quDLm3x/J5uTKUKqUiaQFgPgDbd5cgwn2El9lzJfztxOTIFUPP9lg+b/NeFVOjyMJEfy/LA+YEYq65vqQFbVdyLJWYidxTdQNJi9ju2CNX0muADYk+1+s7ctD8lFBIvFXS221fXrZAiY55jjDGWq3H8nmarlqlLkmUi6TNCOeY/F37LyLN3bXAw8DIzs/rXLdEYpAkJW4ikSiyJA0vkK5enLZflHQB8OHsOlXkCGAP4E2SNrN9VtkCJTriSWAOYM0ey6+VbSuVIxD4CfAJYBlJO9k+umyBBkFTnpNliYX2Uc9zci2ZF4ikfW1/v4uy+xKLvQaumeDcslgl2/4uKXC7R9KniLxtZfTf2wkl7iJD/ruJyTN/tu3VQz8vN18fZBk1vgrsRTxXy5jfXk6831Yo4W8nJkfuQbVij+Xz/In9yvucmJgTiBQp6xGKs63KFWfwZLlwuxk/l2XQ0o5zCCXuHMDxkj7YRd7347Jyzq5TOWxfI+krRJqpkyVtUteIKJlS/a10P6/D9iGDkmsS3EasF6wsaSPb53ZaUNImhBLX2XUSiX6xa+H7ScAunaZaGQHqXLdEYmAkJW4ikSgyb7bt1aI5Lzdv27NKwvbtkvYCfgmckE0eLypbrkEgaQ16V5rtMjDBeuNGYANgOUlb257WaUFJ2xKhrZxdpzLYvk/SdsCpwBGSXrR9Qtly9YvM4/TA7FO3PCfHEmFLAb4t6ZXAt2y3DMMlaVYizPxXm65TRR4mPG3uK1uQEWU5YH3K6b/TiNxe60t6ZZbyIDEa/JvwbttA0jy2OzY8kjQvDS+lBwYjXuUpMxzbz4hw2GtLeqPtW0qUJdEd/yQW4N8uaTXb13ZaUNJbiYV/A1XPuV0bbE+X9GHgNODDks4BPm27UuP8yZCNGbclnitrEGOybijLoKUdPyIW7mcnwphfJukztv/UqkCmIPsWjRDFL2TXqSS2vy7pEeD7wDWSfkL007/ZfqJc6SaPpHWIecy76P2dW0Ul7m+IZ7mAadka0SUTFZL0DsJ7MOfkAcmXmJq8Pdv+B9i1ZkrOOtctkRgYVRvYJRKJcnmEyPW4VI/lcw/cSoYzlLQUcB7wSeB7wHmSTiebXAGP0+GiewWtmwGQtA0xuVp2EpepmhL3V4QSF+CXkuhEkZv9L35e2HXiIITrFUnvJPI2fh74H+BYSfsSSt1u+2MVjRF+DWxJDfOc2D5F0q7EIhTAocAekn5NeGTdDTxD5O1ailgY2Ip4RubK6XNtnzJs2TvkHmLBcP6JTkxUjqOBjxNeZYcB+5QrTqILLgB2JAzhjiByTnbKEVk5Z9dJDBHbF0j6MRFd49eSNrT9cNlyDQJJswBvpDcPrOMGJdckOINQ4s4C/CbznptQISvp9cSCff5OP32gUiZmIOmo7OudhIJzI+B6SX+nu/FzFQ1XkbQc0Z9yL+9ajKNt3yLps4SC00RKmbMlPQBcycxj5zWIdQlo/A8+V1UjGUnNhpyvAD6dfQjb1o6w7cqt02Zz1O/kP3u8TFWNc48gxs6vI+Y/F0maRnj9X14M2S5pQWJe91Fibpf/L+4gomxVFkkrEKHoVwVeRYwbZ+mgaFUjY42hhvVbiEaUxLoZ5da5bonEwJBd1fdoIpEYNlk45HcSeUGWsf2fLsouQky+XgH82fb6g5BxMkiaztjJQ6+eflWdXH2HCNUKk5hc2Z514tOGR7ZgeAWwerbLwGXA8bRWmH2UhkWtifC3a9quTLi7OvdHSTsQyiQT9boZOIXwhn4UeKnTa9m+cBAyTpbM8+33wDp0124i+u+mtp8chGyTRdLXCOOCe2z3atQzZZF0OKHMKeV5mikXfkeELTwaOND2Q8OWY9BIWo+Gl1KuUOr0WVjF5+YawKU03t9nA/vYvr1NmeWJBfFNs3LTgbVtXzlgcStF2fdcJsMswNeBzxCeBV8DTrN9fxny9BtJywJfJNKm9BJdo3L3HMzIp3wzEY5cxHjy+8AJ4ymLJL2JGGN+ihh3ivB+f5Ptx4Yld5mUfb+NM36GHsfQFZzzLAzcQCgvi3O5p4HH6G78PBmD3oEh6RPAt4nwyDnjtV2x/s8Dn7V9+CBlmwwt+uWMw11cqopz8XWBC2nU4yXgEnqb1x3cdwH7gKSVgD/SMBwotuUzNNYa5ioWy7YPAhtVNRqApNcRoed7UVSKCvbJInWtn6R7iPRNv7C9R9ny9JM61y2RGCSVm0glEolS+QOhxJ0DOErSFrYnHJRnIZ9+QSNXze8HKuXkySf6LvweaSRtDuxX2PUwcBY9TK6qRhY2bXPCi3r5bPdaNHLdTsQ/gA9WSYFboLnvjXxfzNi58P2bhOV8razGbD8paQPgC8Si/ZwdFHuWWLg61PYLg5RvkhxFeA4sLmln20dNVCBRDSR9Ofv6W2KRfSdgO0kX0b2HfxVD3uVGYycQ1vZjDpUgTt+wfWVmjPUZoo02BW6RdCWtDZbelhXP6/7dqabArQKSip6bLxGLwD8EfijpcbrzDHz9AEScFJK2JAzn5mDE77NmbD+WRW45k8YC/eeAz0l6gohMkd93S9JIGZP/H54BtpkqCtwKMV4/7LZvVnFc+jngtYRsTwLfAKbZvrNMofqJ7R9L+iNwEPAh2j9XXiAiZh1SVQ/cJmr1fCzwaRrrJxcAO9i+p1SJ+oztG7Nw0b8kUqIU23Lu7DMeFxL5PCsZUl/SqkSbzUtn/XOk1sdqXr+bCUXnkhOdOILUuW6JxMBInriJRGIGmfXv3wlvFgiPkE/Yvr5NmbcAhwPrEYOhx4HX265cSGVJd9KnCXvVrJsl/QF4N1G/XwF72H66XKn6Sxa+6Ht0F2LyeGC/ivbHC+hff9xg4rOGR5YPan7g77ZXKFueQSNpfiJ32QZECKdFCE+lpwjr7OuA84GTbD9elpzdIGl3wqr5eWCnbnJRT3XK9FKqs4cSgKS5gKsJg57JLMBU0uoeQNK3aCyYQvu2K/4Pvmv7gIEJVmEq6hkInbVh8dzK9cvMO+lqxoZNvpvePLB26q90/UPSmkSe+uYxS3PElCK3EcqMywcpW9WowP22Q7+uZfvYfl2rH0i6FVgOeBlYz/ZlJYs0UDJP+HVoPXa+JBlIlI+k+wnjpIeIdZ5KRhLqF5LeTaSXWp/ol808SCgOf2n7nOFJ1h2SZgduBZbJdv0eOAbYBtiCeL9tSChAlyacOTYnDCueAvYn3nOVjIw1Ber3MaI+TwNL1ulZWOe6JRKDJClxE4nEGCTtTHjVFh8O1wMXA3cRL9q5CS+QdYDV8qJZmV1tHz00gRMASHqQyC3x/9k787jbxvL/vz8OQsaIkCFjJMqsyCyZvyWFChlCgwb9qL5J47dJA2VKylRIKKXIlAipyHQUypDZQTiGcD6/P6572fs85xn23mevvdZe536/Xvu1nmevde/nup+9hvu+r+v6XPcBy9l+vmKTSiPJR+5NOMxWJyS8C/4L3EA4zE60/ffBW5iR9CSRtfLjOtYby4xPqh8OsAshDzqJkDQ/nVjMf5jIKp6QutYPL5MaOHH7Qe2cSfBSpvHhxHjjRSJr/HSSQ8n2yJp0Q4mkTYhrrxPFiauBz9i+tFSjakwNnEqNve4knQLsTv2BpBMAACAASURBVFxztwD7NtWxJGlOoi713sBajF5LbxrxHPwhcJLt5wZmYE2o+nprMpKeJhwMl9jesmp7MhkASc8Q8+0zbe9atT2DRNISjAgwsH1ftVZ1hqS9iGeVgVNs75neH/Menvp7PLAN8ASwdV2f+bNA/yYBlxNzgUatqTS5b5lMmWQ55UwmMx22T5T0CkK+qRj0rJFeo1FEpb9I1N3LDtxqKOqzXNpkBy6A7X8AhxS/pyzIeYGnhiXLcRbgLmAVhkOqKDMjdzJj9tG66dUNJo81B00ta431kZ3bft7F9rmVWVIiti8D3pTqb46b4W97clV2Zl6iVuosfWaTtH0K2ML2gxXaUiqpzMHxwPGS5iUCBUdedzfYfqo6KzMN5wninLujakMy9ULS8sCSALYvH/Cfv5d4zj0x4L9bOclhOxRO21HYLm1fADpSarF9XypjdTaRtXq6pDVqusbS6P7ZflHSO4mSd3umQLNP2H6oYtNmmib3LZMpk7ywlslkZsD2NyVdCXyVlkzyeFxOOHBrGcU2KCStQziusH3ygP/8PYT81iyXEZAG3bUbeFeNpK1JTrcKalv+AVgVeP2A/26mvzSufnjTsd10J+5yxPl4VVMduO0kB2120tYc23dVbUOJLEJccxc12YE7kuSo/WPVdmQGj6TDidItVdSovoNw4i404L+bqT8fBQ6kmgDJ64nx13ID/ruZmWNN4nz5k+1HOm1ke5qkDwBbEzVL9wCOLMfEmaLR/UvqQwAXEOt8uwE7S7oCuJFY/+pIWrWCtaBxaXLfMpkyyU7cTCYzKravAjaW9FoiC+SNzBiNfh0h95Qla4P3EdIt04BBO3H/RNQIfO2A/26mvmxLkhICBj24/S6wL7CmpPVmlXpxqXbgUsTi2+wVBHP0i7vpU73mTKbPPAvMDdxctSGZzKBIdSPnh0ok6h8knmsPD/jvZjJVsTBRY7GKcdDpwAbARpImNaVEwGhImotwomxIa+w8yfbmI44T8dwHeL7pilMTUFUw5THA24ENJS1p+96K7Bg4kuamNa8bthIxi6Tt7SPef+m+Imlu2zOUyLH9oKTLgS0IFZzaOTlpfv8OZ8bn0MuIOr+bdflZdXN0Hk5z+5bJlEZ24mYymXGxfStwa9V2DBlVTLCOAt4DrC9p5VnBsS5pfkJWqphYDVpaKjMGtm+V9EXgc4RM0Za2R06wGoGkZYD/B+wKLDBi98kjjl2MmCQK+LPtrw/EyC6xvWzVNmTqiaSPADsRGUqbT3R8CfyLuOfPW8HfzmSq4otUl4F1C+FgefWA/24mMyvyI+BQ4FWEPOjXqjWnHCQdTIydF25/m9Ed568gyrTMDVwDvKl0AzPTYftiSb8i5GtPkrRNkp9vHCloYGeiFvyGtLLiZ3j+SloE2CX9epvt3w3Kzg6ZM22fHvH+k20/v5II3h2NO9O2rhnYTe8fjL6u2O1aY10Ds5vct0ymFLITN5PJZBqA7WslnQDsQzjNNrf9aNV29RtJ8wH7ExOr1WgN9EabWC1Kqz7KjbZPGZSdmZB1lfQCUaPzOknHEPVnbrHdiJpKknYFjgNezoyTjhkmFSnqdxFC3WAbSUfn2nqZIWNFokZmVZPmc4G1iEylxiPpDcyYpbR3tVZlKqKqDKwfENlyG0tayPZjFdkxEFJplGLxvrjuZrM9coy5IC1n0r9t3zBQQzONxPZTkt4BXAh8WdJswNebkpEraQ7iOb518dZEbWxPkfRjIpBlPUkrNDUwtOa8m5jHbQVcK+lTwG9tT6vWrP4haWXgDFrlgCY6P6cABwErAA9IWqpm/4/HCCfmy0e8366ssRJjOzkXTdtFxthfNU3vX5NL5DS5b5lMaWQnbiaTmQ5JWwFzAU/bvqiLdlsA83TbLtNX9icmG3sDN0r6MnCO7furNas/SNoYOA1YvHhrvONtPyRpM0IK/HFJZzQ1ariu2P6ypKnAt4BPpBcR5NzpR7iWY5W0yHZq8SvwOHAVsDwxYRyLEwgn7tzEItZZJZpZSyquH54Zbo4FPgksI2lP2z+u2J5SkLQzITW2SvvbhPN87xHHLgb8FZgDuML22wdkZmbW4BdE9tu6wPeJumWNQ9IriSzIt7W/nbajBa1MBY4nxqT/IhbxM5mZxvbVktYj1Fy+BHxI0nlEVnw3dQLrOL46htY19ixwEnAJcV/ZcZx2pxJOXIBtqKf0aaOx/bSktwGfJcYn5wHPSPoHnZ+XVam4TIikVYArCVWl4t7/NNGvkU5CIDoj6Wjg20T2/CbE+VwXbiMclSOVNG5s+3lLYIa1uyR3vk76ta7B143un+3GOjqb3LdMpkxquTCayWSqQdKqwG+JweqRjDLgGYftgA8DTnK+d5RgYmYcUuTnvpImA98kJJaPkvQfuptcLV+imT0haUPi3JyT1kL2ZGBBWk7d0TiWWGRbkBjE/7pcSzMFkhYgopm3pHXuVZVJ1FdSBs4PiP5MI6Qu/8/2c5KOYnwn7i+B54kx2ObMgk5cqq0fnhliUkbOe4nr5hhJz9n+adV29RNJxxI1xaGzLKUHJV1MlFTYYVarV5cpF9vTJO1AzAneLWkh4GDbjalLLWkJ4I9E5m1H4xTbzyeFkS8Cr5G0vu2rSzQzM2vxLFH7fS1inrPv+IfPgKnZ+ErSWsBehG33Alulsk1Iest4bZNj+3HCwbYR2YlbFYcQmacm7pXzAGt02HYsuezKkTSJyDJeML11KfBp29eked0Hx2n+MyJYGSJLuU5O3L8CbwZeN+L9qwnJ4XmB/SSdYPu2Ecd8kbj3GLiubEN7pOn9y2QymenITtxMJtPOzm0//6DLtj8APpJ+fhfwlb5YlOmYJFF1FK0JcrEQtSAz1uoc9SOo4eQqRUqeDrwsvXUy8Cnb93cwsTqbcOQK2ILsxB0IkuYBLgNWpyGO2xHsR1xXBj5v+4udNrQ9VdItxKJHpwsfTaWJ50amZGyfJ2lrIoPnVEkfB35O91lKtaujLulLxP2l4AJiQXBrIoN/LE4inLgispS6HcNlMmNi+2FJ6xP1OT8EbCXpNrq75lxjKfCzgKXTz5OBLxDX3edoZf+NxunEQjDAW4mF40xmpkjPt58TylhNCoLci9Zc872FA7cLriOeg6tMdGCm/0j6ERGEOcOuQdtSArsDKxPn5jnALp3KItu+T9I/ibqqa5dnYk9cQiRZLCppVdu3ANh+RtIpxPNtfkIe+8dE4Mg8RFb8xm2fc9JAre6cpvcvk8lkpiM7cTOZTDtFFOydxSCoU2zfLOlOYBliUJSduANE0uzArwhHJcw4oRrmCdbewBLExOpY2+M5bafD9qOS/g68FlizJPsyM/IJwkFp4AXgh0RW7k3AYw2o7bVN2j5CLGp3yz+I/89yfbMok5m1+DPwU0JaeU26v7/PUEe9aiStCPy/9OvjwDtsX5r2Lc34TtxLgacIyb9NyE7cTP/ZkXBUQowpV0yvbqidE1fSTsD6xD3hCuBttp9O+8Z1Ttu+Q9K/gSXTZ8wqnE+MfzJ9RtJqRM3YOdvevotwPjxGjKmHleIZdpPt3/fQvlCYWLJP9mQ6JCmg7EErSHwykYF6E8N/XgL8T9o+CxzQQ13bm4hyOt0+E8vmAkL6/+XAroQUdsFnge0JBYr5CGfoaFxo+ydlGjkTNL1/05ESG7YGNiT6tRAwaaREuaJu1dzp1+dtPz9QQ3ugyX3LZPpJrRYvMplM5axCDM6v77H9dcCy5AjZKjiAlmytiEHtS04zhntytX3aPknIOHXLZOKczPXKBscu7T/bPrcyS8qhiNa+osc6y4+lbScZ8plMpg1JbyTk9RehWVlK+xFzMwP7FA7cTkiSt39jdFm5TGamkPQF4DPp12Kc2e01Vzull0QxXnkB2KNw4HbB34h6fOOVUWgUtn8D/KZqOxrKoYQD10Sm+z62r6nWpL5RBOT2Kl06NW1HrU+aKZV2Oe9vAIfarus9vRfWojWve7iH9lPSduH+mTTz2H5W0uaEQ+yxEfsek7QR8BPgTaM1J9TPDijd0B5pev/akXQwEejZfo6NpaL3CiL4Z27gGkbvf21oct8ymX6TnbiZTKad4sHZy+C1vd0ifbAl0x3t8kYH2T6qMkv6z+uJQdzltp/qof2jabvguEdl+skKxHd2dQMduBATCICHemxfZFh0G+mdGQ6uJuowZfpMyki9hOkDIJ4D7mD4A5aKaPM7bJ/dQ/u7CCfuq/tn0tDwXWbN+uKlI2k74H/b3ppClKZoQpAgtLJwr7J9Zw/ti7nPK/tmUWZWpshWnQpsafuBKo3pM3Ol7bM9tp8/bXuZC2ZmjkJd6Z80z4ELrfv3nT22L+ZztVtft/2ncfbdDWwoaQNiDLoE0Zd/Ar+2/ffBWNk7Te9fKpd2LpGlCh0E0NmekuSjDwTWk7SC7dvLs7I3mty3TKYsaveQyWQylfI8MAetSVa39NouM/MUWdQ3NsyBC63ggnvHPWpsigHhbH2wJdMZzxCOypurNqQk/kOcl/P12L6ovTdl3KMyQ4nt04DTqrajoXyGcOAauJ+Qbj/X9nOVWtUfliH61WvW1RNp2+t9aeBImp+QxlwImL3XOsVpAScv4pRDIUFoogbsfranjnP8sLFo2v6jx/aFGsfL+mBLx0i6ZAB/xiOlDDOlUyhMXNQwBy5EwMOShCOlF1Zr+5zMYJmUtn9ooAMXIhhwTmIdrBcWS9vHxj2qpti+CriqajvKYsj7dwzwtvTzs0T93kuA3YgyF2NxKuHohCgDdWRZBs4ETe5bJlMK2YmbyWTaeYRwLvRaz6OQEst1kgZPEQF6baVWlMNUIot2nh7bF7WTssNscNxNZFD3+p3VnXuJhbY3dNtQ0jy0Mn96XTTO9BFJSxCSmiPr8Cw/4rh5iJIBAI82cIF1GNgqbf8LbGa7SddQIRHZa5ZRkf3da5bTQJA0H7A/sDuxKF8EWs1Qp1jSosDB6dcbbZ8yKDszL7EOraCJvXosIVBnivHzpHGPGptCfejxPtjSDZtQrkT1WFKGmXJ5GFic3pVe6sythFLE+pJmt91xFr+klYnnhWnmXLfu3EtLZamJPEgEwK3cbcNUo3M94n9zT5/tyszCSFoL2Is4t+4FtrJ9a9r3lvHa2r5a0uNE4OtG1MzR2eS+ZTJlkrOSMplMOzcSk/Z10qJ2x0haktZCzy0l2JYZn2LS0GsEaZ25nzgvu67zl2RaNiDOy3/12a7M2JxHfGcbVG1ISRS1KldJ9Tm74UO0nNuDyKTJjIGkuSR9j5DVOgL4H2BtYqFq2dGaAH8gnpUXDMjMzPQUNfUuaZgDF1qBRouOe9TYvDZta5ulJGljok79V4lAn9lo1VedQUbN9kPAZkTG9XckzTnymEzpzJ22lzbQgQut62XZHtsXc5/7+mJNd6jDVyfHjzwmUw3FHHrJcY8aTn6btgsTi/fd8E1a52Uefw2e3xP//9dXbUhJXEX0b21Ji3fZ9u20xm09qYmUhaQT02u3Htvvktr/sN+29YOm94+4Txb3vfcWTs4uuC61X6WvVvWHJvctkymN7MTNZDLtXJi2k4jJUjccQSuKPU+uBs+FpMlH1YaUwB/S9g2Slh/3yBnZg1btxMv6ZlFmIo4hslKWldTtQs0w8JO2n49LGZoTImlz4Avp1xcIOaBMBSQZ1z8CBxASamM6kgqShOjx6ZjVJK0+AFMz01PUOG9itsMdxLm1XrcNUyDdGwln0t/6bFdfkLQhsYi/OK3rbDIRqDUex6bjFwS2LM3AzFgUzslaZ3jPBH8mzq/1JS3YTcP0TF8q/Xplvw2bgE07eH2SUC0QoWpzGiGPvRNxLe2Ufj8t7RchK/rJ1H6zgfUmU3Aq8T1sImmhqo3pMz8mypEAfFPSmydqIGlOSScA29IKlji9NAszY3Es8CKwlqSuxyhDwLlpOzvw9U4bpaSH9izAn/XTqD6wJ7EWsn6P7ddJn7Fnf8zpO3vS7P4VNdJvsv37HtoXpcjqGBTU5L5lMqWRnbiZTKadk2jV8niXpO+lTMYxkTSHpO8TUpQQNdl+XJ6JmTE4mlhgW0XStlUb02eKCZGA70vq6NklaTVawQgGflqCbZlRsH0fIZX5HHC0pPdUbFJfsf1n4GzinFwLuFzSmFnHkpaQ9HXgfMJhaOAE23cPwt7MqJxKyGGLkC38FOE8m8ix3u7Af9uYR2XK4ra0fWWlVpRDEUi3uKR3dNn2S7QC6X7XP5P6g6S5iIX3lxHX3MnAq22/jriXjsfZtCRvtyjNyMxYXEN8Z13LTA4J56XtXMD/dtooyYJ/r+2tc8c6tgxs/368FxH08GVCoedY4np7r+3v2/6l7YvT9vu230vI3B5HXKNfBhbscWE1M3OcSgSvvpz43hqD7UeJa0yE/P+lko6TtBkwf3GcpHklrSnpU8Qzvz0Y9GO2nx+k3Rmw/Vfgc8R3d6aklSZoMlTYPpcIgBOwm6RjJwrQlbQNcDURmGbgYtvXlG5sZlaiUB+6rsf2U9P25eMeVQ1N7lsmUxrZiZvJZF7C9pPAZ2hlSBwA/F3SpyStL2lRSfOk7XppcvV3orYZxIP4MNuPzfjpmTKxfRtwYPr11IlqSQwTti+hJeO0JfBLSa8e6/gUWHAgcAWxKGDgLNtZ5ntApPNvKvBp4ns7SdKf071kB0kbS3pLJ69qezIu+xD1vURkwF0h6W4iswUAST+XdAORNfgJWnLn1wEfH6y5mQJJmwLbEfeGm4E1bH/N9rW0skRGxfaNtGrVvalUQzOjcTqtLKW5qjamz/yYVrbj9ySt2EkjSYcRmQgmAvHqWDd2b1oLNsfY3tP2RBm4wEsL/39Pv65Zkn2ZsTk+bTeQ9NpxjxxOTick9QE+JunQVONwTCS9jpDNXJk4p/9i+6JyzewcSUsRgblzAF+3faDtJ8ZrY/sJ2wcQWWhzEuO2Zcq3NtOO7WnAzkRm986SLkxBqY3A9veB7xLP8UnEWPp3QBHsKWIcdi0RnPRqWusSX7R91kANzryE7a8Qaw2LAX+V9A1JGyRlmyawB5GMALAv8G9JJxPZmgBI+rCkIyX9nQgAKrIAHyHGOU2juPaaWgu57v0r5jm9KqEU1+ZTfbCl3zS5b5lMacxetQGZTKZe2D5W0qpE3UYTNaK+NEGzYgB0jO2jSjQvMwaSlibqa34I+DZwiaRziAyWm4kJcUcD1JpmB74X+BMxcXwb8C9JVwGLFAdIOgJYEdiYiPAuzst/0Qo0yAyGy5j+fCscnd3WjzU1HavYfjw5A38KbJLeLibzRd8Lh277gvAlwC62nyvdyMxYFIuFBna3/WCX7a8HtqJVgzQzOH5EBJitCnwNOKhac/qH7X9L+iaRqbQocK2krwFnMELmW9LCxDn4UaYvo3BYkv2uG9un7ZPAIT20n0zUvVqhbxZlOsL2ZUlx54PAGZI2sz1lonbDgu0XJO1DlIKZnchC3UPSz2irtSZpe2KM+VZgc1rX5NPUb/H+A8QC58PAZ7ts+1ki83GR9Dmf7q9pmfGQdGL68U7CebQ58DdJt9PdfM6263ZeAmD7Y5JuJNSSCglz0+rXyCCKx4kM3JMGZGJmBJJeHPHWnEQw6sfT/k4/yrbrOq+7QdIOxJhrMeLc3L3YnbbfaWtSdPp+YEfbTSzzUayzNNVRVvf+PUysLSzRY/siAOjh/pjTV5rct0ymNGr5AM1kMtVi+yOSbiEWSOdjnBqBiaeAQ2wfU7pxNUCSbNctYu9OZnSavT29uqGWTrO0uL058HPCcTIJKGopFf3+aNq2n683AzvYfnwghtaLKcBdFf79kfeNjmf4w4LtB9N5+R7gY4Q871hMJu6pp6ZMi0x1bETcN661fUMP7R9I28X6Z1KmE2w/I2knQpr8Q5JeDhxq+5GKTesXnyMc1G8nxl9fYkQgnaQptBa+oXVvPcn20YMwsgdeT1xzl9vuZbGsqIXcVc3STN84iFDX+CRwk6QvAWd3mk1dd5Kj+j1EkMg8wEqEMhG0xpjtcsnFNfcUsGtSaKgT2xN2X2b7hW4a2n5e0qVEmZxtmTWduOcTmXVVsCczOmlFBLB0G8RSSycugO0TJZ0JvB/YBtiAeOYVPEcE7/4KOG6iTPJZhMNolQkaNGL683LkmkMjsH25pDcAXwF2I+Tlx+IFQv7807YfGOe4oSRlWG9FfNdVrieUwpD071ZCjWB9SbN38zyXtDLh6DShbFA3mty3TKY0ardQn8lk6kHKyD0d2A/YmqgVOHfbIc8QdbJ+A/xgGJxkKXvgkpn8jNmIAftuI/fZ/jDw4Zn5/D5QTLLGimYeWmxPlrQWcDCREbLoOIc/TkTLHlHTrKTSsX04cHhFf/5y6itN1FdSQMcpwCmSXkUsRC0BLEAsej8IXG37zsqMzIzkVWnbq8T6M2k7br2sTP9J0sEQC7sfJDLGdpd0Od2rTnyhFCNnAtuWtAtx7/4UrTq37c/1hUY0exH4ku3PD8TI3lg4be/tsX0xlsmlgAaMpH+2/foCEbxyJHCkpP/QXWbg8iWY2Bds/ywFsB4JbDpyNzOOpy8DPmz75gGY1y1Lpe245QHGoXCYLTXuUQ3F9m+I+W1VjDZ363Y+V/sxeAroOTK9SEFZCwBTbfd67jaWVK6qypJVjVlTGI+kzrO3pEOALRh9XncNcIHt+yozdARt4+PRWHeC/e3MQWRIvpWYL5koUVUpTe/fGPyWOAcXJuY7P+ii7TdprQte0H/TZpom9y2TKQ3VL5ksk8nUFUnzEVGyT6b6uUNFWmzazPZfemwv4CeEFOqkiY4fJJLupE8Tdtuv6cfnlImk2QkJybEmVlfa/m91FmYymbGQdBThgHMV91JJU4laPD+0vV+3tqUAp12AR20vMtoxTUXS7sSkG9t7VfD3pzF6llLXz7+6PcdHkupRfpQoIbDSKIfcDfyaCFb65yj7a0Nb9vCptvcYsa+Ta+58IqDwPtuvLtveuiFpBZJcv+3fD/hvj3bNQXe15ERF9/tekLQ6rezA0caYv0o11GuJpCeJIKPLbG/eQ/uLgM2Ap2w3pd7lUCBpj4mP6owsP1w9kuYFlibuIXN02s725aUZlWkk44yPGeX9jj+WqFm6lu3JvdrWD5rev9GQ9ArgDqI8wlPANravTPtGHTtLmhM4mlA5MHAfsJzt5wds/rg0uW+ZTJnkTNxMJtMxyXE7dM7bNuYDzpe0oe3bumkoaRKRgfsuahjdbHvZqm0YJEly5er0agxpsr8GUaNlPjrMOrJ9cpl2ZTIN42Eiw6jXLKOitnPj5NMmwvZpwGkVmzGrZCndRci0fywtdixOmzNpyOT77icyiF/XbUNJcxDONBM17oeGJNe3JNH32XtdmLd9O3B7P23rkvGur8ZlZyWZ/V6k9uvCXYQs+0aSlusmyEPS8sDGxPV2d0n2dWLHTCkndYh7cXKXSXa8Dj/pvv8RYFdgZXobn+R12kwvjHWu9fqcvhs4oEYOzqb3bzpsPyrpf4GjgHmBSyX9iKjb/FKAVVo/WonILt6fkCku+FgdnZxN7lsmUyZ5cJDJZGY1FgEulPTmTiVwkgP3NOCd6a1nxjk8k+kaSbsRUtzr0Ntkf5Zy4kraGlgX6imJOuykLKSCm22/WJkx5XADkRmxgaS5bXd8T5e0NrAicd3VPohE0hJE1vCGhNN6IWDSSFlTSfMAy6ZfH62xg7DOksGlYftRWnVhh5E/EE6lN0ha3vYdXbTdg3Bem5CwrTVJtWZ/YHeiZld7psjsI45dlCgRAXCj7VMGZWcX1F6dJTMDvyaut0nAGZK2SlKs45KCRc5M7Zw+pyo2odxgm54UHDKZ8ZC0JnAerbIdjQtyKYMUPFKoTeQs5N4YbXz8OVp1QzuRiDeRmToFuBG41vWR7mx6/0bF9veTGstBxLN5n/QqEDOWTijuO1+0fVb5VvZGk/uWyZRFllPOZDKzDJKOA/YlBnCTgbekhdHx2rQ7cAU8Dexk+6KSza0VktYBVoGc9dlPJM1NLJhtU7w1zuGj1WSDIZIo7BdVy/E2nTbJqrtsL1e1Pf1G0v6EHJOBz9j+atu+Mc+tJON+ObB+aruz7XMGZngXSJqLqBm0D9NL+I0qa5pq0d1NSN7eZHuNQdmaaT6SNgMuIq6b3xGyadPSvvGuudWIWmXzA9OA1W33Wsu6dCRtTIwZFy/eats96vNK0p+J7P7HgcVzKYjMzCJpSaJG+HzprQeIxe4zRiuHkwIP3p2OWZw4b58EVrXdax3rmSKNQzqlWNCaaAw98phZbgwp6XDgfdSoRnUaf8xPd3LDlWWJj4WkhYFbadWAh6gjfgfhNOo4Y8z2yLrcjSaNAw4kzsucaNQn2uZz37f9kart6TdN7187kt5PzOsWTG+1O3JGPvseJ7JUh0LZocl9y2T6TX5AZjKZWYn9iYnV2wmH5K8lbW776dEOTg7cnwA7Mws7cBPvIxZZpzGLZX2WzAnAtunnZ4FLiayX19LKsJ0PWAZYnVjgMCGneTY5i6BykiN+OyKL+jV0txBVOym/xPPEGLH2maY9cjJwGLAYcLikO22fPl4DSYsQkvqFA/e2Gjtw5ycyFtegwywQ21MlHQ8cAqwmafUkKZrJzDS2L5H0e0KmdUvgl5L2t/3v0Y5PEsr7Al8h7qkGzqq5A3dD4LfAnLSy/CYTi1KLj9P0WOD4dNyWVJv9OHAkLUiSzqujY2YYsX2vpP2IgILZiPPvOOD7km4lAnaeJurmLk2MOYt1IQEvAvtV5cBNdOLEWhv4MnHNPQX8ghi33EOMk19OKFCsD+xISDY+B/wv8Of+mzwULEyoblQ2f0jz692IwIF1mN7p2Ql1lRv+BNEXE0EQnwZOtv1UpVYND7XJWpa0GLAW3c/r6qYQdTlpzlK1ISXR9P69hO0TJZ1J1IPdhigzMl/bIc8BfwJ+BRxn+4nBW9kbTe5bJtNvciZu6VBhcQAAIABJREFUJpOZpZA0JyG3simtjJDtUo3V9uNGc+DuaPviwVpcD+qU+Ziygnt1mO1dmmFdImk94CriPLwd2Mr2XWP9r5NjZl/C+TQvcAHwrtGyKppOHc5HSQIOTa95e/kIanA9jYaku4iaMyfY/kDV9pSBpHcC7Y7bi4g6PNsBOxHX5Rq06vDsRix4i8is2NL27wdpc6dI+iXRD4CHgG8DlxCS7e9h7IzA1wN/I/r+adtfG4zF9UHSR0jff00DLIYWSa8mFmEWS29NI56Bi9AKXPoOIVe+MXFfLRZ1/wmsbfvxQdrcKSnz/XZgifTWycCnbN8/0fMqSdg+RPT1SNsfG5DZtaBuGViSZiPOx14W72sV5ChpByJYcJG2t0db/Gl3nkwB9rV9bpm2zSySdiSe2XMQDupDx1vYTWPorwEfAP5LjJ9/MQhb60TV42dJKwLnkNSd6M1xV9ex8/VEwO00YBPbV1Rs0tBQ9XnZZsebgC8SY5CenMp1PDczzSSpGCwATLU9UnZ4qGly3zKZmaHyiVImk8kMEtv/TRP/y4A1iayHU4loYOAlB+5PgXeQHbi1QdKuxMRqZuqz1caJS9T5K3i/7bvGOzgtTh2RHDSXEo6lHxGBBpnBcwate0TTuJXIXlmmakPKwvbPklPpG0Sm0hbpBa1F7r+1NSm+5xeBA2vswN2UcOAauAXYwvaDad+4k2DbN0p6CHgl8Kayba0pK1J+LcYJkbQRrSylwpnU6bytFs6wkdj+t6TNgZ8TTrJJwJuL3Wn70bRtv6/eDOxQVwduYm/CgWvgWNsf7LSh7Ucl/Z34n6xZkn11p/LnqKTXEBmaO9NbYFahnlIbbP9S0ipEhuCeRJ3Osf7XDxJjym/ZfmQwFvaGpKWAkwgH7tdtf2qiNmkMfUB6Dv4/4CRJa0w09s70jyQ3/HsikKf9PJxKSGS+MFq7IeI1xH3gyuzAHT4kfRQ4ovi1x48Z2gwpSW8HdiCuz4eBX9s+o1qr+kcT+2d7KnH/bBxN7lsmMzPUboKfyWQyZWP7KUlbA1cSC7bvlDTF9gdTzcOfEpLLhQN3B9uXVGdxRtIRjL642w11m1gVi9d32L6y00a2b5O0B5FF/j+StrU9S8kvVk36/+9M65yaDPwMuAl4jOFfiDqTCHDZSNIrPEHt8GHF9rcl3QAcA6zQvittR95rbgcOqHlAz3vS1sDuhQO3C64HtiIcSpkBI+mVRGDZFiN3VWBO37E9WdJawMFE1s2i4xz+OJGZe0RazKkz26ftk4QkebdMJjLTVpjowEz/kfQO4BTgZTTkWiuwPQX4tKTPEOfYG4hAnXkJCeKHifv+ZA+PRNsHiMCWh4HPdtn2s8BeRHbyBwjJ28xg+BQRSFDIDf8fcLrtO6s0qo/Mlra3VmpFpmtSOYQjaN3/XwD+yJDP61LgRFH65QrbM9zvkvrEWYTkfDu7S9qfUKyr5Ris6f3LZDKZkWQnbiaTmSWx/YikrQhH7hLA/pIeJ6Qz35EOyw7cGpDk4NrlBacQNeOGemIFLEksZFw34v2XFtEkzWn7vyMb2r5Y0s3AqoTTJjtxB8v7237+OiGbOSyLn51wGnHNrQIcTZtSQdNI19LKhBOmqMOzBEnCichOuoaow/Nz29OqsrVDNiLuIdf2WNP2gbRdbNyjMn1H0jxEfa+VaJgjqR3bzwBflPR/RE3Lsa65K0d7/tWU1xPX3eU91j8sAmUW7J9JmU6QtBpRPqVdNvluhn+MOR1pjHJLeg072xPX22Ujy+FMhO3nJV0K7AJsS3biDpIi2GUa8FbbV1dpTAncA6wMzF21IZmu+TitWvaXAXvY/nelFvWHLYENiX59d4xjPkmrjMzIsedbgB9S33lg0/uXyWQy05GduJlMZpYl1R99K7FouiBR27IgO3Drw4FpayJL+gMNiZicP22njHj/mbaf5xtlf8FfgdcBa/XZrszEFAv2t9s+dKKDhw3bz0raGfgtoVQwP/BR2/+o2LRSSIvbv0yvYedVadvrQn1x/5mnD7ZkuuNgYgHYhOPoRKJu803AY7ZfrNC2vpOcL1en17CzcNre22P7YmFxtnGPypTBIYQDt3By7ttA51LTWCpte62TV9TOXWrcozL9ZilazvcmXmMXEioma1dtSKZrNkjbKcBOtp+s0pg+slXa/hf4zcidkl5GyMsXgcgXEIHhSwMfJtQp3inpm7b/XL65XdP0/mUymcx0ZCduJpOZpbF9s6TtiYnX3MRC2jPA9rYvrdS4TMFaxOD7PmAv289XbE+/eJpw0s4x4v32un/LMLYTt1j0XbzPdmUmpvjOGlnzStJh6cdfAvsT9ZcnJ+nhvxAShs+M0Xw6bH+hFCMzYzEpbXt1+C2Utk+Me1SmDNrrm+9i+9zKLMl0y1QiGLDX4Icl03as532mPDZJ26doqyGeqTXFGGz5Htsvl7Z5LWywPEFIed9RtSElcTQh0b2ypC1t/65qgzIdsyCx1nBxgxy4EMHeADfZfnqU/dsS434D59ou1OiQ9DeizACE6lcdnZxN718mk8lMRx64ZjKZWR7bf5T0TuAXRCTfdrYvq9aqTBvFouilDXLgQshurUorg6egPdtxAyLjdjRWLcOoTEfcRUgNN1Xy9HBmrCEtYPX06obsxB0sDxPZLr1mGL0xbR8Y96hMGSxHXHdXZQfu0HE/sVD4uokOHImkOYhnvYF/9dmuzMQsQvzvL2qqA1fSEoR88IbEs2EhYJLt5UccNw+wbPr1Udt1fQ7cRYyBN5K0nO1/dtpQ0vLAxsR3fndJ9mVG5w7CibvQRAcOI7b/IeljhDP3FEmb2765arsyHXEv8BqaF8D4SuJeN5aS0lZtP39zxL6fEiWDXgWs33/T+kLT+5fJZDLTkSWbMplMo5D0Yi8vot7hJCIb9+IJjm9Efawh4p60fa5SK/rPjYRjbOUR7/+JlgNtH0mTRuwn1XNeMx3X8eJVpm/8gfjuXl+1ISWiEa/R3pvolRk8NxD/+w0kdVWXTdLawIrEfaWJUod159m0zYu+w8cf0vYNyUnUDXsQ9YAhavFlBkvhuH24UitKQNJckr5HjBOPAP6HkHpdgZazdromxLl8IyE7WVd+nbaTgDMkdeQUlPQK4ExaihW/HufwTP85nTjHNhptbtMEbB8L7EXc06+V9G1Jb05lSTL15Xri3FxuogOHjEXSdizp+Y3S9hHbV7XvsD2NUF8SvaselE3T+5fJZDLTkTNxM5lM0xCxAN2LA6FwnGXnQ734E7ASUWeoSVwOvJuQ3XqF7UcBbN8j6Qpi4rE6cE6St72ZyEreEfhW2+ecN1izM8B3gX2BNSWtZ/uaqg3qM5+v2oCZRdLSg/g7tuuWyXM+sB0h1X4Q8NVOGkmaHTiy7a28uD14/kVkKM1btSG90CbDXio1lWj/GSGjKeD7krZJC4TjImk1WtkhJjJDMoPlFiI79dVVG9JPktPoMmANOpzX2J4q6XiiTvBqkla3fUN5VvbMkcT1Nh8R0HiTpM8BZ4wmhSppPmK8/TlaJUieYvpnXqZ8fgQcSmS+HQx8rVpz+ksKCp/uLeAj6YXU8fKCbed12sFyDPB2YENJS9rutb593ZgzbWc4n1JQy2uJscdY5YEKNYa6BiE0vX+ZTCYzHXlwkMlkmkivTtjsvK0nRxG1StaXtLLtv1dtUJ84n1bAwXbAyW37DqU14dg2vUbjEeA7ZRmYGR3bt0r6IrEgeHqqfXV71Xb1C9tD78QF7mRGSeh+Y+o3lj4ZOAxYDDhc0p22Tx+vgaRFgFMJOTEDt9k+p3RLMyM5l6gBv0HVhvTI4ZR/zUENJdptXyLp94RM65bALyXtb/vfox2fJJT3Bb5CLB4aOMv2LYOyOfMSPwC2BjaWtJDtx6o2qE+cCrwh/fwQ8G3gEuDDxJh6LH5COHEB3kaoO9QK2/dK2g84jVCWWxw4jgiguJWQSX6aCHxcmljIL57VImrG79cgR81QYPspSe8ALgS+LGk24Ou2Rzo/h5X2QHIz/fMwrzHUGNsXS/oVMR8/KQVi/bdqu/rA44Tk8GhBShvTOlf/MMp+aN0363qNNr1/mUwmMx11W3jKZDKZmcJ2lolvGLavlXQCsA/hMNu8yFodZmzfLelbxMTjlSP2XSVpX+BYxn5WPwzsaPuRci3NjIbtzydp9c8D10k6BjgbuMV202oqDTOz1MKZ7aclHUTIFs4BnCZpL+AMYMniuJQBuBLwVmA3WrXHXyQynDKD51jgk8Aykva0/eOK7emFbq63iVRTRts/CCdxr7yXUA5ZjHB+/UvSVbTk/pB0BCFZvjGRcV3071/A/gO1NlPwC+AaYF3g+8T9cKiRtCnhjDCRabxFUe9X0liykwDYvlHSQ8S49E1l29orts+U9CxwAnGNiXjmrZZe7bTfR6YA++a649Vg+2pJ6xEBZ18CPiTpPOI8/Q8d3uNtnzzxUZWgEdvM8PBuYh63FSGF/Sngt52oatSYvwOLEiVW5rL9bNu+ndt+vnyM9q9K2yllGNcHmt6/TCaTmY7sxM1kMpnMMLA/MSHeG7hR0peBc2zfX61ZM4ftT46z70RJfwQ+DmwGLAFMI2qb/Qr4tu3G1XAbJmx/WdJUQt76E+mVJdPqwd10thi4BDEeLr40EwuJU4GXE3XN2vc9D9T6vmP7Z5JeDXyDyFLaIr2g9T/5W1uTon8vAgfa/v1ADM1Mh+0pkt4LnAUcI+k528Mkr9tJBv8CwIGEs0XAvwkH2j20rrmlgPVoZVY8R0gdjut8qhrb/5a0OfBzIvNvEvDmYnfafjRt2x8SNwM72H58IIZmpsP2NEk7ABcB7071VQ+2Pcy1qYtMWwO7Fw7cLriecGTUuoyJ7V9KWoUYe+1JLMiPNQB7kJDz/VYOfqycZ4n73lpEFvW+XbY306sX1YIcSD7cpCDItwGfJZRFzgOekfQPOg8wsO3Ny7Oyay4myjPNT5RX+SiApI0IJ6eB+2z/dYz2b0zH3FG+qT3R9P5lMpnMdOSFw0wmk8nUnhQFu6+kyUT9uKOAo1JGQTcTq+VLNLPv2L4V2K9qOzIzImkBIrtxS3I97dphe9nx9ic5068RE/5phPTkycCf2mvqpVp66xILxLsSzqezgUNsv1CG7f3A9rcl3UA4v1Zo35W2I8/V24EDbF88CPsyo2P7PElbAycBp0r6OOEU7DZLaaysg9KYSIZd0muJMgJzEk6iT9i+dJzjNyee92sQteC3Tc/E2mJ7sqS1iHqPHyQyRMbicaIcwhG2pw7Cvszo2H5Y0vrEM+FDwFaSbqO768629y7RzG7YiLD52h5r2hZ1Ahfrn0nlYHsK8GlJnwFWISSkX0lkuj9FqNZcD0y2XedM/lmC9Hz7OTAXeeycqR+HAAfRUgKZhxiDdEIh3VsnTiT6NBfwYUk7EVmnryd8AQaOH62hpFWJZ4CB6wZibfc0vX+ZTCYzHdmJm8lkMpnakxwuRwF7Mb3E4oJEZs+EH0H9JlaZIUXSPMBlwOrMIotPyZm5AbAmIV04H/AkUZf5r8BV7c7PIeB7hET7w8BOtq8a7aDUp4uBi5Nk9rmE43d+us8eGSipxtfKwPbANsT3twRxz5xKZCZdQ2T2/3zIJeOaxJ+BnxLSymumVzfUrlazpJcD5wDLABcQ19xz47VJ5+/6hNztVsA5ktax/VTpBs8Etp8Bvijp/4C1Gfu6u7IhNfeawo6EvDzEc33F9OqGujhxC4nIXmssP5O284x7VI1IDtpb6L3PmZJJZRzOJQJ5Cu4isnIfA2obGJdpPpJ+BLxvtF2DtqVfJIWQjxMBnSaUTpai1adbgSPGaL5L289XlGbkTND0/mUymcxIajXBz2QymUxmJJJmJ5wMhRzoyMnU0E6uMj0zhVj4qYpPEJHZJhadfkhk5d4EPGb7xQpt6yuSliGkxXYDXjbOoc9K+gnwJdtVfjcTImkLwgFrYNexHLgjsf1HSbsCvwPeL+ks2xeUaOpMkxa2f5lemZoj6Y3Ab4lAiSZlKe0FrEw4h947kQO3wPZzkt4H3EnUcH4/cGRZRvaTlKl/dXplaoykLwCfSb8WgYLdXnd1ChSclLa9jkUWStsn+mBLJlNwKOHALRzu+9i+plqTMhlIpSz2oHX/nwz8jDSvY4gDDGwfJ+lB4HO0soqfIQLrPmb76ZFtUvB8oQT2AlFuoJY0vX+ZTCbTTnbiZjKZTOYlJKmGcmMH0JKsFZHF85LDjCGeWGV6w/bhRL2iqmiP3t3F9rmVWVIikt5OOKjnZ+IF7bkJB8vOkt5v+5yy7ZsJ9knb621f0k3DlB14HSHZuDdxP8pkZhpJSwOXML26xHNEra5hf9a9m3iGX9ptPUrbD0m6hMgmfxdD4sTNDAeStgP+t+2tKcCvGe4x5sO0MpJ64Y1p+8C4R2WGkfMJBZUq2DRtpwJb2p6lzi9J85JUbOquKFEBhxHlE6qiXVnnG8ChNVwP6Zk0Tz03qaLMDzw8QUmYlxElZACesf2fsm2cGZrev0wmkynITtxMJpNpAJI269YZMcpnzEbUhdxt5D7bHwY+PDOfPxO0SxsdZPuoiuzIZApWIBwSVzfYgbs1cDqR0VM4cB8F/kRkxU0FXk7Io64LLJyOWwA4XdL2ti8csNmdsg7x/V3fY/u/EYvca/XNokwmMgEXIM7N+4mM/3M7zVqtOUU9+nt7bF+0G6q69pmhoBjbmnjm7deAGsU3AEsDG0iaO0l8d4SktQkZaTMkWeSSliCC6zYkHNcLAZNsLz/iuHmAZdOvj85qTkQA278BflPRny8UJi6aFf73SclmP8J5/UbaZKQl/Zeow3kJ8IO6K9iUje3HiKCZqijUlf5Jwxy47aRn24TPtxRk8PvyLeovTe9fJpPJZCduJpPJNINzkiP3L700liTgNGIRZAYnbsWsQkysbswO3OElRaCvQaue6mydtLN9cpl29cgzxGLMzVUbUgZpofOHtMaJdwKHAOeMFtksaRJRT/BrhJNlDuCHklbqZvF4gCyetnP02L6Qqlx83KMyw8jVwLwV/e2t0va/wGa2/1GRHWVQZBcv2mP7ot38fbAlUy++C5xV4d8vgnruB/ZqSJ3i84HtiLHWQcBXO2mUype0Z7r/uv+m9Q9JcxHZe/sw/fNcjC5vLeAPwIJEpvUaoxyTKY+HiXHTQ1UbUiaS5gS+DnyQ1lxnpJrNy4D10usQSd8DDhmm+0+a1y1NPN87Hk/bvrw0o3qnGNf/oakO3Ewmk8kMP9mJm8lkMs1gPuB8SRvavq2bhskBcyohU1jHicu0tL22UisyPSFpNyLTZR16qzFXRyfu3cDrgXmqNqQk9iIW2gz8BdjK9uNjHZxqAJ8t6SKiXuw6wBLpc44u39yueZJYQFu3x/brp20lcnhJdrd0bN89iL9TJ2yfRgQ0VcESxDV3ScMcuBCyrMsCm0qatxspSUnzAZsR/5sHyzGvIzsOG8Tfsf2FQfydmUXS/MCSRNbj7L0uzNu+Hbi9n7Z1ydxpe+kwOVAm4GRCnnQx4HBJd9o+fbwGkhYh5gLrE9fabXUui5DOv8sIR2xHY0vbUyUdTwSlrSZpdds3lGfl6CR5+LKx7c0H8He64RZibLlk1YaUhaS5iXHwBkx8Xhb7JwEfAdaRtIXtZ0s0caZI191HCCnaleltXlfHNeh7aaksZTKZTCZTS+r4AM1kMplMbywCXCjpzbbv66RBcuCeBrwzvVXHrLl7gNfSe9ZcpgLSQsaZRB1DGH+i7wn2143zgNWJRZomsl3avgjsOp4Dtx3bTySn/a1E9sH21NOJexMhb7eipHdPtLjdTupfITV5U0n2TcSdlL/QVNeFtulomIzmo0TG6T1VG1IClwF7EgFnRzN9mYSJODq1c/qcqjicwSzw1taJmxzq+wO7A6vRem7PcL+QtChwcPr1RtunDMrOLrmPuDfU1nHSLbaflnQQIQ89B3CapL2AM2hzoElaDVgJeCuhwlMEpr0IfGCgRnfPqURteojMzm8T0rQfBt4zTrufEE5cgLcR0tODZhPKvZeMlYlcNacCWwCbSFooSeg2jR8Ab6L1/78ZOBG4khlLkbwZ2INWIMIGqf17B2pxh0hak5j/vKp4q0Jz+s3vibH966s2JJPJZDKZsaj94kwmk8lkOuIHwL6ErNGFkt5i+9HxGoxw4Ap4GtipbEN74EJCUnntqg3JdMUJwLbp52eBS4HXEA75IsN2PmIhY3ViodHEAsfZ1HMBquAYQiZtWUl72f5R1Qb1mdWI//8Vtu/opqHtOyRdTjhJVyvDuD7wU8I+CNlnOnHkStqVuNcWVJWxWdCkBbSuaKiM5m2EE/eVVRtSAscQjlsBu6esv4PGUw6RtBLwHcLBBPG9Vh0U0s01N1Fw0mj7a/vck7Qxcc8rZOTH/V/YfkjSZkQtyMclnVHTTNdriLHJylUb0k9s/0zSq4FvEEFVW6QXtM6zv7U1Kb7PF4EDbde2XqCkTYlgMxPZnVvYfjDt+894bW3fKOkh4j77prJtHYdO7yXFdzXRvaSbz6yKU4G9gY2AYwkFqMYgaV0iGMKEitQnge+OIs87lQg8uBb4jqQPEUEIk4DdJB1pu1bqU5IWBi4AFm57+wXgDmAK8HwVdvWRYwn1oLUkrWf7mqoNymQymUxmJNmJm8lkMs1gf2Ji9XbC4flrSZvbfnq0g5MD9yfAzrQ5cG1fNCB7u+FoIiNgFUnb2q51ja4MSFqPkNoyIZO4le27JB1FOHGxvVfb8fMTQQiHEfUoFwXeZfvJQdveCbbvk7Q78HPgaEnP2z61arv6yCJp26vE5R2Ek3SRiQ6siBOJe+aahJTmaZI+DJxCLOjfTdwT5yECY9YnMnvWp+UkvA6oynl/N505e5Ygxvrt2XL/oZUJssCIfc8TdSFrzTDLaE7A6URG8SaS5qqzpGK32L5W0hHEorYJx+ytkq5l7GuuCNwqvuNvVbyw/fkOjlkAOJAILBDwb6J/99C67pYi6iC+OrV5jnByj+t8qhJJGwK/JWrBF/fAyURQxHi1wY8Fjk/HbUk9a6weD7wb2EDSa23fWrVB/cL2tyXdQJxfK7TvStuR98/bgQNsXzwI+2aCItPWwO6FA7cLridqkL+2r1Z1zqYTH8LawJeJa+4p4BdEzfaR95L1gR2JsfNzwP8Cf+6/yTOP7WmSdiYCNXeWdCHwcdtVqZr0m/YM2k/a/k4njWx/T5KI+uAQAU+1cuICnyDWGUyUJPk0cHI3pRHqjO2/Svoc8CXgTElbNrCsRSaTyWSGHOW67ZlMJtMMJM0J/IZYHDBRk2c72y+MOG40B+6OdV60kbQn8EPgCcLWnmqvZQaDpKMJJ5mBt9i+Mr1/FJHBatuTRmm3IpGxuzhwju2dB2d150h6S/pxTeCrxIL9dYRT92ZiMb6jAVYdz2VJjxLOiDNs79ZD+58Qi+KP235Fv+3rB0mG9xJCSrKbwbCIhe5Nbd9bhm0zi6Q5gK8BHyWyQU4jMt//1B4YkaRR1yVkbncl+vYd4JCRz406IemXtCS/R5PRHOv+8noi88zAp21/bTAWd0aSoP8TsCrwPdsHVWxS35H0DeDjTB88MObhbT9/y/bBYx5ZAyS9FjifkOa9HviE7UvHOX5zIpt8DeBfwLZ1dCCmrPfbiaAQiHvJp2zf38Ez/RXENSrgSNsfG5DZXdHWjxuBzWxPqdikvpIcRNsT5S02IL7LBQhn4INEoMGvgJ/bnlaVnZ0i6R/A8sQzbYMR+8Y9J9MxPyYcZU/YXrBkc7tG0o6E9PUcwHHAobafGOf4+Yln/geA/xJBkL8YhK3dIOnE9OMchApUoaJxO92NnW177/5bOHNIuhF4HXCv7aW6bCvgLkLufLLtWinZSLqeUE2aBmxi+4qKTSoFSfsT4+AXiOCXs4Gbx7v+MplMJpMZFNmJm8lkMg1C0rxEhtKaxET4Z7bf3bZ/EiEl+g6Gx4G7dPpxW2KxfnbgHNLEiu4cZneXYWNmeiT9jagrdLvtldre72RxbXMiAMHADnXMvJY0jenPuV7rj9l27VRRJN1EOJIatxDVjqSFiHtKN/U5TwE+NpFcfZVIOo6QGX6EUFi4qoM2bwLOJTItTrS9b7lW9kaS0byY0WU0O7m/PEDIaP7K9o6DsbpzJC1POAJXIDK9D7X9SLVW9RdJmwBfITLIJuJq4DPjOUPrgKSXE9lvKxElIHay/VwH7V5GZNhtBfwdWKdumU2SPggcRVxzx9r+YNu+Tq65m4mMxytsbzwAk7tG0mzEOflJwun8JeBs27VXJpgVkfQEkYn645HOvA7PyWMIh+cLtucs295ukLQUEUwwH/B125/qou1Xgf9HBLyuYfuucqzsjVHGztDj+Hms77ZK+hAAeRoRUFe7AMgkUz4vNb6PzwySXhz5Fg2a12UymUymGeQHTCaTyTQI209J2hq4ElgReKekKbY/KGl2woH7dloO3B1sX1KdxR1xJzM6zN6eXt1g8nNvUCxJS3K2nZe+R0lzjlYfz/bFadF3VSKrrnZO3MRIGcK61yLrhkuJ//8Skj5i+8gu2h5AyISaCCipLbYfA/aU9BWiTtumRKZB+6Luf4EbiP/Jibb/PnBDu0DSFoQ0uYFdO3HgAtj+Y6r5+zvg/ZLOsn1Biab2yrDLaI6JpMPSj78inBB7EfVjL6f7gKUvlGJkH7B9GfAmSasQ19wbCMf6vIRs6MPE93Sp7clV2dklexE1VZ8B3tuJAxfA9nOS3keMc1YC3g90c78dBNun7ZOEHHm3TCbKfKww0YFVIOmfbb++ACxGfAdHJsdFN9mBy5dgYmZGCgfeSMdLpyyUtnXMrvsAMD9xH/xsl20/S9yLFkmf8+n+mtYXRhsrdzt+rmsWytxp22sgztQRn1MnZkvb2qlF9ImRTtuR6w6ZTCaTyVROXszOZDKZhmH7EUlbEY7cJYDgbMZ6AAAgAElEQVT9JT1OLBC+Ix02LA7cdooJ1li1vDL1Yf60HSlJ+Ezbz/ONsr/gr4Qk2Vp9tqtfXE59F5H6wamEEwngm5JesH30RI0k7UtkthacUoZx/SbVvXrJOSFpAZJDyXZt61SOwT5pe3239/cUQHEd4VTbG6ijE3cj4tq7tseatg+k7WL9M6lvHM6M95WXAVukVzfU1olbkBy0w+KknYh3E9/dpd1mTtt+SNIlhNTtu6ifE/f1RN8u7zFLuFAtqJ1sbWJZRl+8F2HzAh18Rq9ZW5neeJioB9uVUkgbb0zbB8Y9qhq2JwXBdVvWwPbzki4FdiHUi+rmxN2ragNK5hFi3t1rwEoRBFJH9Y17iEClOjqY+0VeV8hkMplMrclO3Ewmk2kgtu+S9FbC2bQgcGjb7mFz4N5NXhwbNp4mnLRzjHj/8bafl2FsJ24xkV68z3b1BdubVG1Dmdi+RtKZxELgJOCo5KD9EREccheRMfByYGngTcAehIx7sZh9pu1rKjB/pkmO22Fz3hasQ/z/r++x/d+IBe66BlC8Km1v6bF9EUgyTx9sKYMmZyk1mWLxvdc62UW7OmZyLpy2vfatOH9nG/eoahnvGmvMwr6k1QjH50LA7LZPrtikXrmBGHtsIGlu289M1KBA0tqEUpEJufa6UTimex2DFNnFvTq4S8P2SVXbUDI3E0pEG0pazvY/J2pQIOk1tILUbi7JvpnhQkLBZO2qDSkD23V+PmUymUwmA2QnbiaTyTQW2zdL2p6YeM1NLEQ9A2xf9/py7dhetmobMl1zDyHHu/CI9//R9vMGRMbtaKxahlGZrng/8BpaTsHVmT7LdjSKxe5riUzOzOApAh9GBlB0SiFTWcsACpoto/n5qg3I9EyRrbloj+2LdvOPe1Q1TCWCAXsNfFgybccK2qqa11RtQJlIWoaok7orM2YVnzzi2EJKWsCfbX99IEZ2z/nAdkSw4EHAVztplMrKtGe617FcR/Hs7jWgY7m0zet8g+d8olzDJOAnkrayPeFYI9VUP5X4zkyUVKgbRxMS3StL2tL276o2KJPJZDKZWY08uMtkMpkGk+ocvhP4BVHbcbtUjy6TKZMbCTnklUe8/ydaWWL7SDrW9nTOmCQFvmY6ruMo9iaR6lqvC9XVtrT9tKRNgCOA/egsi2oacBxwcDeZMZm+8iQhwbtuj+3XT9tea7qVTWNlNG1nJ+7w8gAhy7uppHm7kR2WNB+wGfHM67bG8yC4nwh+eF23DSXNQQRsGfhXn+3qC7bvqtqGskh1zo8jVDNGZhTPkLFv+0FJixC1qreRdHSPEtplczJwGCGLf7ikO22fPl6D1K9TiWecgdtsn1O6pd1zFxHIuFEP2ZzLAxsT/bu7JPtqi6TDgfdRXX3qHxLKV4sRAZB/kXQwcJ7taSMPliRC9vobRMmj4hlw4sAs7hDb/5D0McKZe4qkzW3XMWO4lqRrc0kA25dXbE4mk8lkhpTsxM1kMpkhQVKvmUcFcwMXx5xxTGx7lno2SFoHWAVgiKXl6sblRI3AlSW9wvajALbvkXQFIRm2OnCOpMMI6bB5gB2Bb7V9znmDNbs2bEvUpDUV1rZMjtgDJX0T2JdwNLyB6bM8nyekey8BftDNgmOmFG4iFuBXlPTuiRa225G0Gy2ZyZtKsm9mabKMZmOR9L5+fVZNn9OXAXsSmYFHE46ETjk6tXP6nLrxB8Kp9AZJy9u+o4u2exDZn3XtW+lIWpCUYW17YI41Se8gnJYQDtzHgauIDM+Vxml6AvEMmRvYGjirRDN7IgWZHQScToxHTpO0F3AGrczvQj56JeCtwG60sslfJLIK68iviettEnBGyuZ8bKJGkl4BnJnamXpmGZfNwsxY43pg2J4qaT/gHCLwcXngbGCKpGuYsRTJusArU3MBLwD72H560LZ3gu1jJT0DHAtcK+k44v5wYycZx7M4HwUOJM7NWWqdJZPJZDL9Q3Yum5TJZDLDgKRpxOC/l/pcxc1+ora2PWmCYxqFpKMIh9m0Wc2BXRaSlqaVdbNX+6K7pA2AKyb6CCLj7nW2HynHyvrSdk7W8nqUtAAwL/BUqh87VPQhIKagdkEvkvYBjifu+c8Ce3fiyE0ZWycQC/cG9rP9wzJt7QVJ+xNOLwOfsf3Vtn1jXjdJRvNyWllYO9c0C6s0JH0E2In4/2w+4L9djF9mltpdc/BSMNhVtMZYFwAH2b5tnDYrAd8hHEwilAw2sH1tyeZ2haTNgIuI7+93wDZFVtkE19xqxLN+fqJvq9vutZb10JL+RwcywHM3OY7/SchgTwO+BPyf7ecmGl8kaddHCUfD8bYPGITNvZAyA79BOMwmur8U1+aLwAG2TyjTtl6RtCQR2DhfeusB4HPAGbafHOX4+Yigyc8RZRBEKHKsarvXOtZDSV3GzpLeBfyAGCcXjHZ+ts/JnyIcuGeWaVuvjDJuFr0902v5DC+bupybmUwmkxlucgH3TCaTGS56ceAW7XptO6uQ/z99ImWbfIvICnjliH1XEVmdL9I6L0e+HgZ2nBUduMOA7f/4/7N333GSlVX+xz/fgSEnAclJQMEsCiwgKAqigiiLLkpwEUUFFUQRUH+7iuuuawIRIyqIBAWRYADWAIiARAVFiYYRBcmMwpCZ7++P81yqpqequ6q6wu3u83696nW7+96n5tRUvuc557FvnYoJ3EJN28le6uY4Yq1pEQnZkyVdImk/SZtIWknSkmW7iaT9JV1CVGxVCdyrgW+O7BaM7wQarZAPl/SmiQaUNpo/ov5tNAft6cC25TIKvTy3psJzjpJ4PYJGfK8EbpB0maTPSzq4PNcOLr9fDlxPI4ELcGTdErgAts8HLiTifAXwA0lrtTte0mxJ76KRwDXwvZmYwG0y7MfuO4gEroGP2T7c9iOdDLQ9D7iOiPf5gwtx8mx/jngO/YHWrxFj//YH4FV1TeAClMTrO4jkO0Ri9hjgbkm/kfRDSaeW7W+Au4nKyCqB+wQxCWtGJXDrxPapRNeaE4HqedfuvewR4nPN8+uawC00Zusx+6by5+aUUkppSphxs6BSSmmqsp0Tb9KUYfuQcfYdJ+mXwPuJFr1rECes/kQkWz5n+66hBJpmqkF2NBgZ2/MlvZZob121zNyCxlq3E/kjMYFiofXb6mCat9Gczr7V4XGziPa7zwWeVv72MHAajaRGLdk+TNIs4n2teo3YrFxaaX4dOdL2oYOMb5LeTKxpvyrwauDPki4FVq4OkHQEMVHgpUQFWnX7/gzsN9Ro045lezfwqR7G30QkcNfvW0QDYvs8SRsBOxO3e0viM+XyROvaO4DLic+Wp9f1va2Z7e9KepjojrEy8VyaDTynXJo1v47cA7zd9llDCTS1VZYW2bt0wNgK2ISY1LoMUXV7FzFh7pdTaELk2ERuSimllIYok7gppZRSGjrbNxDVBikNVacTYhQLiFcJpTcC+xKJpP1qui4nALZvK23LP0d3a3OeCLyvWsO6rmyfVioBqzaa25cLNBLtv2ka0txG8122LxxKoOlJtvfpdkxZw/jzRFJmdaIFdq3X3bN9iKSzgU8QEycmOtl9GdEW/IKBBzcJtv8maTvgdGBjYt3NF1e7y/agsm2+zb8HXmt77lACTZWNiPvlYtuP9jC+WoN1+f6FNDiO9cF+UC7Tgu0fSHomcDCx3vZqtH89uYPonnFkdrCpl5KgPbdcpqycSJ5SSimNXiZxU0oppZTShCQtRZzUnd3pmNJae0oqJ4bnAhcBF0k6lljr8puSlrR9zEgDHIft+4C3SPoE8DbgZcDzgMWaDnsU+C1wAXCc7RuHHmiPbH9O0m+BrwAbNu8q27Enu/9ArIN43jDiS5Nn+ypJLwHOJtr4nkCs6Vtrtn8ObFUSMC8j2mqOrcC6BrjA9vWjirNbtq+X9CLgA8TafquMc/hcYr3fI0p73jRcK5btnT2Or94nal+1Op3Zvgf4sKT/BzyT9q8l15fPKymllFJKaZrKJG5KKaWUUlqIpEWA3ctlM2ClLq/CTKPPmravlrQ3kVQ6StKFpaK8tmzfBBxW/S5pecoJ4CnUwq+l6dhGMy3I9hOS9iWS8DtL2tX2GaOOqxMlQTtlkrSdsP0Q8HFJ/wtsSvvn3CU9VoCm/vgH8X69bI/j1ynbe/oTTpqMkqC9rlxSSimllNIMNG1OrKWUUkoppf6QtB5wFtFKGHINLABsnyvpBqJd5f7Ae0ccUldK4nZKJ2+bTcc2mmlBpZXvJURV6z7AlEjiTme2HydaQV826lhSS7cSa6m+oNuBpePGFsQkrJv6HNdAlbXQ1waeAixa52UPUkoppZRS6kYmcVNKKaWU0pPKSdzzgPVYMHn7ELFW3mMjCKtOriLWhdxh1IGkNEP8kUjiPn/UgaQ0BVxAPFeeKWkT21d3MfY9wFJEEvf8QQTXT5LWBQ4lOoaMXcP3hDHHrgocTXyuucr2p4cSZJo2JP178+/NEwXG7puMqTYBQdIyROX//bYfGHU8KaWU0nSUSdyUUkoppdTsvcDTiJO4jwGfJ9ZMrXXr4CF6uGzXGmkUKc0cS5TteOuwppTCt4GDys/HSNrW9oMTDZK0HfBf5dfHgZMGFF9fSNodOAZYmoW7hSy0RqztOyStTEwI2VHSl+uecJK0BrAbsDWNKuNFbG8w5riliIl3APfavn2Ycc4gx9N4bJkFJwo075uMsddbO2XyxDuI59ImNNbRRtKjwNXEJJCv2/7LSIJMKaWUpplM4qaUUkoppWa7NP28h+3TRxZJPW1ctrnGakoDJmkW8JLyay1bgc/kCqxUP7avknQGsCvwIuAXkg6wfWmr40ui8CBiAtdsIon0Ddu3DCvmbkl6PY0ks4C5wKXABsAzxhn6DSLxtCTwKuB7AwyzZ5KWAD4L7EvcJ0/uonWiUMBFwArA78iuBYM03vIi03rpEUmLAZ8G3g3Mqv485rDFgX8pl8MkfRE4LNdJTymllCYnk7gppZRSSqnZ04mThL/OBO6CJL0YeDHx//PnEYezEElP9OmqbHsk3xMkrTOMf6fOCYq0gP8G1qG8Jo04lnaOZwpXYEn6yDD+Hdv/NfFRqU/2BZ5FTDraBLhY0q00JVwknU683z+7+lPZXg28f3ihdkfSCsDXiXjnAx8H/tf2I5K+wPhJ3B8QHUYWBbajhklcScsBPycSsR0lBW3Pk/Q14DDgOZKeZ/u3g4tyxvpWj/umPElLAj8FtmTix2W1fxHgQGAzSdvbfnicMSmllFIaRyZxU0oppZRSs8XL9pqRRlEjpVXhnsAnaVTC/HCkQbVWxTaVq0Hm0J+E2HhMfg8ami4T84sCKwEvAPYmThhX6nySvNvnXJ2ep4cz+OccNFr1pgGzPVfSy4DvANuWP69Z7S7bqutG8+PwfGA3248MPMjevYOoODXwMdsf73RgSXZeRyRI61qtehLx+gdwJ/A54n45ANhrnHHfJpK4AK8GMonbZ7b36WXfNPF1YCsarx+/B44DLiE+t80jWpuvS0x23JvGRIQty/g3DzXilFJKaRrJkxcppZRSSqnZ34ANWbCF37Qh6fwuDq8SShuy4Ofmu4Ej+xlXH/WSGKpOytUlqQT1iiVNzhwmnyQ8x/YpfYhlEDpNLs8ClgeeS6w7DrHG9mmMvj17N8+3iRLQrfYPI0mcmpQ1YLcjEn/vo5EYbOV64FPASbZH/VicyI5lezcRc7duIpJL6/ctoj4piffXEM+X64Dtbd9R9o3bTt72tZLuBJ5KJNtS6gtJmwN7EI/L+cAhwOdtj31dn0dMPLgSOErSe4hJCIsAe0g62vaVw4s8pZRSmj4yiZtSSimllJr9gmix+NxRBzIg29J9QqE5IXEbsLPt+/oWUZ/YnjXxUSBJNJJJbyRab84H9qvBmpy30Nn9swbxXaa6b0ysmVpVgyw/Zt9jwN/7GmnqVi+J+fnAV4CD+xxL3/RSgSVpU+DzRIXS6sAbbP+z37F16GMdHLM88C5ico+IyT6XA3+l8Zxbm1gHca0y5hHivqvlWsYzQUmynAicKGk14vG2BnF/zgPuAC6zPWdkQXZvI+I1/eIe19ms3ruX719IfVNV2hrYs0rgduEaYAeijXZK/dJcQXuI7aM6GWT7i+Xz5ufLn/6dSPCmlFJKqUuZxE0ppZRSSs2+CuwDvEDSi2z/atQBDUAvrU9vJNoVHj3CZEtflBP7c4GLgIskHQv8GPimpCVtHzPC2NYbb7+k2UT11UFEgu8kYh3RK2zf33TcssDmwFuA3Ynk0xnAYbYfH0Tsqa1OE/MQyfb7iTWnrwBOmY7rF9u+StJLgLOBVxCP4V3GHzWwWMZN4kraGDgHWIxIEh1s+4Jxjt8O+CxR7fg6YCfbN/Qv4tQL27cDZ446jj5YsWzv7HH8YmVbx4rjbYjXyit7XNP29rJdtX8hpU40rS1+he3/62H89pQK6hquH75t2d7WaQK3yReJyt01gZf1M6iUUkppJskkbkoppZRSvdwD/GVU/7jtX0n6H+A/gW9LekkP1SB11s1JpCqhNKc5QTjd2L5a0t5EQukoSRfWOOnyRaJy+C5gF9uXtjqo3F/nAedJ+gpwFpH4XQ54+5BiTUycmJ+pbD8haV/gD8DOkna1fcao42omaWki8bcuMdFjl4nWS7V9nqQtgO8TVYFnStrM9gMDDzjNBP8gljlYtsfx1Rrd9/QnnL5arWyv63H8Q2W7VB9imWrOIVpsj8rhRAL+S0DXSVzglUTHCVO/9cPXJOK6qNuBti3pImIy3Rr9DiyllFKaKTKJm1JKaUaQpBZr96RUO7YPJ04GjTKGj0qaD3wEuLYkdb9n+9ZRxtUPti8cdQx1ZPtcSTcQrSr3B9474pAWUipV3k6cTNy9XQJ3LNu/lLQ78FPgrZK+Z/vHAww1pY7Y/pukS4jJJfsQ1eJ1sg/xmvAQ8OaJErgV249I+ndiPeRnAG8Fjh5UkGlGuRVYmfHX+G1J0lLAFsR7yE19jqsfFinbJ3oc/5SyndLdQnph+1zg3FHHMU0tWba9TsSZN+Z6ZpqPEN0pUkoppZ5lEjellFKtSXq57fMneR2ziJabe4zdZ/sA4IDJXH9KU42kP3V46GPEydIjgSMl/YOogulkQoRtb9BjiGk0riLW0tth1IG0sW/ZXtPt+0KpDryaOPH/NqKqcCa5DFhm1EGklv5IJHGfP+pAWngT8Xp/ge2uqtxs3ynpfGBHYu3tmZjE/TzwvVEHIem5xGNsE+CpRBXr/UTl4q+J+/fa0UXYlQuI58ozJW1i++ouxr6HqFI1MKnvFgNyF7G29No9jt+kbG8f96hUR9UyH3WccHw3UUW7YY/jq+8Co6yUHhnb99FYizullFLqSSZxU0op1d2ZJZHb07qckgScDOxGiyRuSv0gaRnipOLKxMnRWZ2Ms33CIOMax3p0fqKoOk7ACsDyHYxRF9c/VJKqVor/tD23h/HLU/4PpuFanQ+X7VojjaK9zYjH1TU9jv8NcZL7RX2LaIqwfTLxXjh003ytwH5YomxXGWkUrVUn33vtwlCNm1ITeiQtR7QQfQqwqO1f9HI9tv9AtMseCUkvJdYQ32ycw95cjr0S+NB46x3XxLeJ1vgAx0ja1vaDEw0qazVXrx+PE5M76+a3RLvnLcv69A9NNKAiaVPg6cR75GUDim+iGIaRGLft7Ybw7wzbCmU74WN5BH5PvB5uLWl9251OBEXS02is9fz7AcXXd+V73TrE5/3ZnY7r9b0ipZRSmkgmcVNKKdXdssA5kra2fXM3AyUtQpykeSM1TSilqU3SHkQl92Y0ZtF3ysCokrjQfbyTHVcXc2isW3ZgD+P/A3h/uY7p9ll647KdP9Io2lu9bDs+oTZG1apy9XGPqgFJaxCTj7YmqrKeAiwytrq9tAddr/x6r+06VmAdzvRdK3BSSqeQl5Rf/zHKWNqoJu30mmCuxi3Xh1gGStKywH7AnsBzWLAybtExx64CfKD8eq3tE4cVZ6ckfRI4pPq1gyGbAz+TdITtQwcX2eTYvkrSGcCuxIScX0g6oF17/fJaehCxRMBs4v78Rk0nYZ0DvIb47vNe4JOdDJK0KAtWup/d/9A6si2D/b5V2wmCkyFpNvF/B71PmBmkc4gOLYsA35a0g+0JW3aXNdVPIl4/DfxooFFOUpm8cyCxfu9G9Pa9brp9L0gppVQT+QaTUkppKlgZ+ImkF9u+rZMBJYF7MvBv5U8dz2ZPaSKSlgS+S7SJhPG/6HuC/aPwtFEHMMXV7f6cNEkvBl5MPF7/POJw2rkfWJxINvRii7LtdV23gZO0BLF22r4smKxud/JawEVEFc/vqGdL3tTefxPVPiba2tbN7cQkgZdJWsZ2x8+dkhR9OXHb7hhMeP1RKlZPpjHBY9zX+NIq+uVEZf9cSafafnTAYXasrGPfnIg1cGm5/JVYo3Jp4rG3BbAlcZsFHCzpCdsfGmrQ3dkXeBYx8WgT4GJJt9J0v0k6nahMfXb1p7K9mpiIVUcnEOtnrgocLmmO7VPGGyBpZSJRVq31e7PtMwce6TghdXhcc5eXyRwzdGW973Y2nmB/s9lEhesuwPqMsIp6AscCHyQel5sBv5L0AeCHthea9Fe6YO0EfIZYE716DzhuaBF3SdILgR8Cq1V/GmE4KaWU0kIyiZtSSqnuvg68nTjR9BNJL7F973gDxiRwRbSm2mXQgaYZ5RvECQqIFrQXEInRjWlU2C4LrAs8j0b1xzzgDEZcSWD7L6P891N9lErOPYmKnypR+MORBtXe74h1HZ8u6U0TndxuVqrmq1aTvxtQfJNSqkB+TiRiOzqBaHuepK8BhwHPkfQ8278dXJQjUee1AptbtHdiUWAlYm3mvYnkWeVb/YyrT34OvIV4P/sy0GlygnL8ssT99vM+x9U3krYmKsQXo/EaeD0xMWK8qv2vAl8rx72C0VU/LqAkIw6jMYHsx8CB43WzkfQMopJzhzLmA5JOs13HiQXYnivpZcB3aFQwrlntLtvqc3/za+n5wG62Hxl4kD2w/aCk9wKnEJ8bT5a0D3AqjduHpOcQybFXEkvFLFV2PQG8c6hBL+hlHRyzKfA/xPPtAeD7ROKyeXLB2kRS+nXEWu6PEF1Qrup/yD05nvaTqrYrl17MJ15XaqV8zngHcCaxXMwGxHeZeyRdDvyFBSeGbE6svQ3xf/I4sG8nbc9HQdJKxOvkSk1/fpxYr/4e4LFRxJVSSik1yyRuSimlutuP+FK1K/BM4GxJ27X7IlgSuN8G3kBTAtf2z4YUb5rmJP0L0WrLxFp3O9j+i6QvUNrR2t6n6fjliIkIHyFORq0CvNH2/cOOPfXF4mVbm6qrSpfr0VXJpA1Z8DvB3cCR/Yyrj75D4yTxsZLoJJEraXdiQlBlJGvDduAkIrkHcCfwOSLpcACw1zjjvk0kbQBeTayrOJ3Uea1AaLRon4xzupmUMERfIRK3AvYsVX/v7SAheBSRYIL4v/nyoAPtRal8P4XG6/oJxLqwfy/v6e8eZ/gZRMJFwPbUJIkL7E8kWky8Zu5le9zHp+2bJL2aeG18Uxn/LqLitZZs31HWud0LeB+N185WrifWBj6pVeVgndg+TdJaRBXjLOKxtX21u2x/0zSkSlI/AbzL9oVDCbSFif5tSa8jEriziefOB8dpy/ul8vn5U0Ri+n+Iz87f72PIk9FuolWvFZwPAgfZvrzH8QNl+0eS9iI+Sy1D3M6VaXQkatb8f/AAkcA9Z/BR9uxg4vOwiY4vHwZO6KbzREoppTRomcRNKaVUa7ZdKqjOJU7ebw6cIek1th9vPrZNAvd1ts8bcthpetu76ee3TlTVWk5QHSHpB0TF7iuBbxKP0zT1VK0Zx+0IMCLb0n0yqflk223Azrbv61tE/XUcMbHnhcCSRJXSAcCJwOXALcTr/lI02oTuVbZVhd3VxPOvVkpV2WuIGK8Dtrd9R9k37lqptq+VdCdR+bLVoGMdpimwVmCzXk7ezycSpQf3OZa+sH2lpCOItVVNvH/dIOlK2j/nNi3Dq/+PI21fOdTAO/c2YA3itn3V9nhJ2wXYvlfSjcTkrRcOKL5evLxs5wH7TZTArZTP2/sRr0NL01lV5UiV23YicKKk1YjK9jWItZznES1cL7M9Z2RB9sD25yT9lnht2LB5V9mOfa35A7B/nb/vSFqb6DYwG/h0J+26y+fn/ct74KHAtyQ9vwbdZFp1TdibuH9uJF4bJ2Kik889wLXAuXWf3Gn71PLa/1FgN2LyS7v3vUeICvKP2a7rEh2VKhFt4DW2Lx5lMCmllFIrmcRNKaVUe7YfLbO3f06cKHsFUbH0puqYksD9DvB6MoGbBuvFZftH25d0Osj2zZL2Bn4K/KuknWzXpXInjaO8vqxJnLTaljjRc+0oYxpHt4mk6qTjt4Gjx6mKGTnb8yW9lqhOfUb58xY01rqdyB+J94U6VmJVlbYG9qwSuF24hmiFunFfo+rSDFwrECKR2enkiceISp8/A1cAp9i+ZVCB9YPtwyTNItYRrV5fNiuXVppfg460fWib4+pg57K9n0Y1ezeuJ7rEbDjRgUO0OvF4vKDbpJDtf5aODq9l/FbStWP7dqLd67Rg+zxJGxGP0R1pnaC+HPgRcHpN39eavRNYDrgL+M8ux/4nsA9R+flOolJyZJq77VTK53uAn9k+cMghDY3tPwF7SzqQmDS2CTGBbBmi6vYuYrLcL22POwGtRp5GvGZekgnclFJKdZVJ3JRSSlOC7QckvQq4hFjX8N8k3WP73ZIWJRK4u9JI4L7WdjetRVPq1Jo0KvqaPXkSX9Jithdqt1tOyv0eeBaRtKldEre0lzwfWAL4te2O2ylKOpY4oXM/sN3Yavlhk/REu13AuyV1XHU1ZqyB03sObHC6qZyqkklz6l790cz2bZK2JFoNd7M+54nA+yZaU32EtiEeV1f2uKbt7buZVEwAACAASURBVGW7av9C6snxzKC1AgFsrzfqGAbN9iGSzgY+QaOyfTyXAf/P9gUDD25ynks8Xn/RY+vM6vVkhXGPGq4HiAq52yc6sI1qAkm2Eh2xUmn8g3KZ6namrI/d7WdD249JuoCYSLcTI07itlFN5rln1IEMQ0nQnlsuU92ssr1hpFGklFJK48gkbkoppSnD9t2SdiASuWsA+0maS1Rkvb4clgncNGjLle3YEzUPNf28bIv9lV8TLXlf1Oe4+mUX4iS9gU93OfZnRLWEiUqeM/obWteqhGurhEOv65ZBtMU+bhLjB2KUa+ENU2n3/BZJnyDaob4MeB6wWNNhjxJrw14AHGf7xqEH2p3Vyva6HsdXrz9L9SGWyZpRawXOFLZ/Dmwl6ZnEc+4FLFyBdQ1RAXr9qOLs0kpl22ur7uoxPWvco4brz8Tt6nVCRzVuTl+iSSmsXba9VmdWXULWHveoEZkJk3mmsb8CGxHLdKSUUkq1lEnclFJKU4rtv0h6JfALovLhg027M4GbhuFBIkk7e8zf5zb9vC7tk7jVSd+6tip8Vdk+BHy/y7FnlXFLENUSo07iwuSStZXmdctOB4633a7KNw2J7ZtoaoEqaXlKQmkKtfGrLFK2vT6unlK2o26HPSPXCpxJSoJ2qiRpJzKP+CzZ6+SHNcu2TtV3pxOtrl8uadlunjuSliPW1DX1eP/uiKTnEhMLqtauyxKdJu4mJs5dYLuuSyDMFNVn5g16HL9+2eY5zNRvPyGWoth0ogNTSimlUckPQCmllKYc27+XtDPxpWtJIknzELDzFGjdl6a+vxLtkFca8/ebmn7ekjhx2MqzBhFUH72Q0i7a9kMTHdzM9kOSfk2sGzzySmPbC1VHSZpP3L4vTbd1yyStU378p+254x7cevzyxHp71H2dzlZK4naqJW8rdxEVRr1WGW1Str22UO2LmbhWoKSPlB+vsP1/PYzfnlhbENv/1c/Y0oT+TkyAeHa3AyXNJt7rTVS/1sXXgQOJiWJforu2818iJsLcCnyt/6H1l6SXAp+i/frMAG8ux14JfGgqfk+Q9BziveEpwKK2TxhxSL34C/H5dxtJ65e1VTsiaQPgpcRzrbafTSRtTHQEeaybbgSlu8Fs4OEyOS0N15eJtZY3kvQK2z8ddUAppZTSWJnETSmlNCXZ/qWkfyMqBR8FXlNa/aU0aNcSJ3w3GvP3K2isBbmvpK+OrdYs7cCrJGnHJ7CGbN2y/WOP4/9IJHHXnejAEepHdW4dzaEkqImT+N36D+D95Trye8Jw/RZYB9hS0pLdTKCQtCmxVryJtUjrZrqvFXg4jedd10lc4JXAweU6Mok7XBcRiaUXSNrAdjfve3sTk14M/HwAsfXE9n2SXg+cA+wpaQXgQNtz2o2R9DTgaKKDxr3AG2q8fjgAkj4JHFL92sGQzYGfSTrC9qGDi6w/JK0LHArsTplc1eSEMceuStx/Aq6y3e1SGMNwNvFcWwQ4VdIOZWmEcUlaEfhuGedyPbVT7q/fEffBCcTSIp06lJhs8bik9Wz/fQAhjkvSApM9micKjN03GXWcgGD7JknvI5K5J0razvbvRx1XSiml1CxPzqSUUho5SZNtS7okcJ407jkc2873vdQPvwDeRMzYXrE60Wn7r5IuBrYh1uc8s1Ro/Z5o1fg64Mim6/nhcMPuWLUm1MM9jq/GLd2HWAbhZWXb6xqI0910TXDX3TnAa4g2oO8FPtnJIEmLEifvK7U7wZ1rBaYaO42owBLwJUk72p4/0aBSGfnZ8quB7wwuxO40JVw+D3yYSMzuKOkSYpLHLcSyEEtRJo4QleACHinjNpI0dqLaAkaZjJH0P0Ti68lwgEvL5a9Em+ylidu3BXEbVS4HS3rC9oeGGnQXJO0OHEPchrHvyR57vO07JK1MfL7ZUdKXbT8w+Ei7cjTxXFuWmMz4O0kfBU5t1fJb0rLEZ+2P0lh+5AEWfL+rk92ItbGrCT3d+DIxKWRR4I3AUf0NrSPH03hsmQUnCjTvm4yx11sbtr8q6SHgq8CVko4Bvgdca3vUy1SklFJKmcRNKaVUCyK+2PWSPKi+VGbiIQ3LOTQer69hwRMSHwQuLj/vVC6t3M1oTtJ04j5gZWJduV5U42p50sP2haOOIaUWTgA+AqwKHC5pju1TxhtQTtqfRCQpDNxs+8yBR5r6rfr80o+T5H01Ayqwzpd0IdGq9RXADyTtZ/tvrY4vLZTfDnwCWI64z75n+7phxdyB41n4sTQL2LpcWqk+hy9GVJZPZGTJGEkvJNZCrz6H/ZioNL55nDHPIJJ/O5QxH5B0mu12y16MTKmiPqn6FZhLJKc3AJ4xztBvEEncJYFXEQmo2rB9q6R3ACcTj8fViUT1lyTdwMKTCzamcb5SxHrx77Bd1wl41QTBv9u+qpuBtq+U9HdgNWA7Rvf9YLzv0tP2e3aLyeQiutkcWPZ3elU5YTyllNLA5BtMSimluuj1y+G0/VKZ6sn2LZKOBNZiTKLT9qWS3k7M5G73Oesu4HW27x5spD27lbhdW/U4fivi5OrQ28GlSVu8bB8daRRt9KFrQ6V2J9psPyjpvcApxNp4J0vaBzgVWLM6rlQAPoNowbsHccIb4gT3O4cadOqXFcr2wZFG0drxTPMKLGLN1CuICRSvBv4s6VJiMhMAko4gWpa/lFgztvrs+Wdgv6FG25lWn40n+rw8VT5P70+j4vE7wF62x32MlnapryYSiG8q498F7DvgWLtSWl9/nbgv5gMfB/7X9iOSvsD4SdwfAI8Rnz23o2ZJXADb35X0MJFwXpm4nbOB55RLs+bH4z3A222fNZRAe/Ns4jHZ68SAXxETQ7ten7tPvtXjvumgeTK5WfA9b6q8LqaUUprmanXyIqWU0sxke9aoY0ipG7YPGWffcZJ+Sawt+nJgDeJk3J+AHwGfs33XUALtzS+AFwCrSPo326d1OlDSG4kT4SbWGkxTS3XysK5rIU6ma0Pt2T5N0lrAZ4gkw/blAo2Tir9pGlL9PzwBvGuUVeaS1mn+3fYt7fZNRvP1TgelsnPb8mtdK8y6fb5Nqeeo7b9J2g44naj+W4RY1x0az7uDyrb5dv0eeK3tuUMJtHPTPeHy8rKdB+w3UQK3YtuS9iMSZUvTqJysk3cQkzoMfMz2xzsdaHuepOuA55dLLdn+gaRnEuuAv4WoPm33enEH8E3gyBpPfKxUkzpv73F8NW6VPsTSNdtt1/Adb980ojHblFJKqVYyiZtSSiml1Ge2byBOxk1Fp1FaiAFflHS17T9MNKi0K/zCmOsZCUnnN/1q29u12TcZC1zvVCVpEaLSczcimWTg2lHGNIFp3Xbf9uck/Rb4CrBh866yHXsb/gDsb/u8YcQ3jjksuJ7eom32TcbY6x26CdoLb9xF++HZxPNuF2B94rZdNsnwBqHThOAsYHngucDTyt8eJt4HJlxjdtRsXy/pRcAHgHczfiJlLtHu9Ajb84YRXzdmQMJldeL5ckGrtVTHY/uf5TPAa2mss1onO5bt3cCnehh/E5HAXb9vEQ2A7XuAD0v6f8AziYmDTyWq3B8gOtZcA1zfaZK+Rnp9j1p0zDYNSU4mTymlNBXkB4SUUkoppfQk25dI+hlRAfhU4ApJhwEn2H5k7PGSFgf+nTjhWFWQ/ML2BUMMe6xtWbA1Wqt9k9HqeodqnNbCAt4t6d29XC1xu07vObAB6vREm2IBsyqh9EaiZeZ8omqrri1dn2T7PEkbATsTJ/W3JCr6lyeqz+4ALicq+0+3Xack2XRfU+94Wj/3RbQw7XVix3yiDX+t9JIQlLQp8Hnicbs68AbbtVwjvZnth4CPS/pfYFPaP+8usV3LlvMzxANE6/9eKx7vaLqeutmIeH25uMfH2H1lu3z/QhqckqC9rlymuruIZVbW63F8Na7uFccppZRSGoFM4qaUUkoppbH2Aa4kWiOvQCQXPiPpMqIt9ANExcTTiBPdy9JI0NxJJHVHbbonk8ZrLTyZ23cBcNwkxo9cOTE8l2jpfZGkY4EfA9+UtKTtY0YaYAfKbfhBuUwFt9B+YsN4+6aids+vXp93DwIH2b68x/G1YvsqSS8BzgZeQayFu8too+qc7ceJqug6VkanWId4JeLzSS+qcXP6Ek1/rVi2d/Y4frGyrdPEnpniRmBtYEtJK3TTZl3SU4CtiPfJCTvfDJukj5Qfr7D9fz2M3564fdj+r37GllJKKc0UmcRNKaWUUkoLsH2rpFcAZ9Jo6boccUJ+rObExR+BXW3/dcAhTmS8te7quA5er/qRjH4YuIdooXw6cLztdlW+U5LtqyXtTSSVjpJ0YWl5nvrE9nq97JuCWrUX3ps4+X4jUak5EbPg8+7cbtvC1p3tJyTtSyQkdpa0q+0zRh1XWpCkXYnWwqsSlYRn2z51tFFN6HRgM+Dlkpbt5rkjaTliTV0DdXw8/oNIUC/b4/hq/fF7+hNO6sJPiQ42iwGH01hHuxMfK+NcrqduDidi+xLQdRIXeCWxBrKBTOKmlFJKPdDUW2IipZRSSikNg6RlgcOAdxEVue3cB3wZ+PR0S0ZMJZLmU0602T5wouNnGknXEe0qv2j7vaOOJ00P+bxrT9J5xMSZs23vPOp4ZgJJKxETsCDa8n64xTGzgO8Br2txFb8AXlPH9X7hyarFa4lW3Sfb7rjzh6QTgT2BW4Hn2753MFH2RtLVxJq219l+zph9XyDWa7btRVqMXYqo4F2SWC94+yGE3DNJawC7AVsTFaxPARaxvcGY45ai0Wr4Xtu9ttEeKEmrEJ1qlix/+qDtz3Qw7lDgk+XXh4Gn2751MFH2ZrLvcZI+Q0nitnrs1p2kZYiJFffbrmMb9pRSSjNAVuKmlFJKKaWWSkL2PyT9F7BFuaxKOZlBrC13GXBZrhFYG9OhVfSgXAVsDOww6kDStFK1i87qt4X9kUjiPn/UgcwgryASYybWJm7lEKLFdauW/C8BjgXeNKgAJ8P2fZJeD5wD7ClpBeBA23PajZH0NOBoYCfgXmKd5lolcIsLiOfKMyVtYvvqLsa+B1iKuE/PH0Rw/SBpCeCzxFr1s5t30X698YuIiYS/o6avJbbvlPRpGlWrn5S0C/BFIqn+ZPJZ0qpERfi7iSVJKGOOqFsCt0+q15gpUUEkaV3gHcR71yY02pQj6VHgauI59nXbfxlJkCmllGacrMRNKaWUUkppGpD00vLjrbZrt67aqEn6GnHieJ7tXttVTubfX2fioybP9i3D+HdSmoikE4C9gEdtLzGiGD4y8VGTV5e1HiUdB7wFeARYyfaDY/YvDtxGo7vGT4hW8+sABwCLE8mWf7F91ZDC7pikqvJ2PeDDRCLQwCXEpLJbiHWmlyJu05bEepwCHgU+AUyYeLF9Qp9Dn5CkTYEriNvzK2Db6v4brxJX0nbEfbgY8BhRzVm794HSzvrnRCK21YSzdlXG/0t0hTGwie3fDjLOXkkS0e67miDR7BHgAWAZ4jn25LCy/YHtWq4d3odK3G8AbwUesL1cv+PrF0mLAZ8mnmezqj+3OLS6b+cTSfrDciJrSimlQctK3JRSSimlNGNI+lP58Vu2P9bD+A8BbydONm4w0fHDZPvCUcdQcxuX7fwR/ftzGHwlisnveKkGSsvel5Rf/zHCUA5nOBVgtUjiAs8u29+NTeAWOxGtaw2cZfv11Q5JvwFOLL/uRXQvqJvjWfj+nEVUH2/dZkxV5VmtVzoRA0NP4tq+StIZwK7Ai4BfSDrA9qWtji8tiQ8C3ksjmf2NOiZwi5OAF5Sf7wQ+R1Q0HkA83tr5NpHEBXg1UMskrm1LegMxUeADNBKBAEuUy1jzgSOICQnTjqTZwLbl19pWGUtakliPeEsm7mhT7V8EOBDYTNL2th8eYIgppZRmuPyCn1JKKaWUZpL1iBOdK/U4fsWm60hThKQXAy8m7rc/jzqcEf/7KQ3DfxOVkAZ+PeJYunnOtWoxPNH+Or0fPJWI56Y2+5vbyX92zL7vEJVoqxHLJ9RVq/un08RL3e0LPIuYdLQJcLGkW2mKX9LpwNNpJOyrfVcD7x9eqJ2T9DLgNcRj8zpge9t3lH3jTvKwfa2kO4nH9laDjnUybM8HPijpWOK+eCWNNX2bzQHOBY6yffPQApxAU6V7KxtPsL/ZbGBNoip5feJ+v2yS4Q3S14nHVvVa/nvgOKLCfw4wD1gaWJf4LLk3jYryLcv4Nw814pRSSjNKJnFTSimllFJK05KkpYA9gU/SqMb64YjCqdZOncgaxPe05nXk/kHjJOLyY/Y9Bvy9r5H2kaQnJnkVJtbgngtcD1wJnGL7+snG1qmxrbCbK9362Sa7bhV0Xd62RYnJMS8gTnBv2bTvW/2Mq0uddFxYHngXkXgQ8DfgcuCvNJ53awP/AqxVxjwCfIXRVhm3snLZtotrm7K9e2yFp+35kn5FJNtq1WmiySgfSwNne25JeH6HRgXjmtXusq3a7jYnps8HdrP9yMCD7E1VaWtgzyqB24VriAkIG090YB2UxOz+AJJWAVYFliXey+6wfecIwxvP8bRfm3i7cunFfOCrPY4dKEmbA3sQt3s+sWb4573w2oPziAryK4GjJL2HqCZfBNhD0tG2rxxe5CmllGaSTOKmlFJKKaW2JG0GbEZUEixPnOTuhG2/bVBxjVC1Xtvjo/jHJZ3f9Kttb9dm32QscL110eXtqxJKG7Lgd567gSP7GVenbK833v7SdvBTRHvM+UTryROAK2zf33TcssDmxLqXuxPPyTOIddlG8ricQJU877USTsRrz3JEZecrgf+QdDbwTtvDSGDPoXFie2zL6uZ9k1HHVthzmPxtO8f2KX2IpScTtc2XtDFwDtFq9xrgYNsXjHP8dkQF6/OB1wE72b6hfxFP2mJlu9BjSdKKRBLMwMVtxt9etrVcu9L2PqOOYdBs31EeZ3sB76PRgriV64n3jZNKFWhdbUM87q7scU3b6nG5av9CGo6SsK1r0raVdu/Vvb6HPwgcZPvyHscPWnMF7SG2j+pkkO0vlnWQP1/+9O9EgjellFLqu7p9SUwppZRSSjUg6d+Idb3Wn8TVTMckblWZdv+4Rw3OtjQSYmOTK9u2+Fu3Wl1vXWxL97E1n3S8DdjZ9n19i6i/vki00rwL2KXdOogloXsecJ6krwBnEYnf5Yj1muuouXK4+ffxjD127JidgKskbWX7L5OMr1PjxT1V2rX2opfbNp+oVD24z7H0jaSlgTOJFpk/Jp5341Yy2j5P0hbA94nKwDMlbWb7gYEH3Jm5RNvZtVrseymN1/iL2oyvzhFNtoI+TUKpAjwROFHSakRl+xrEhJZ5wB3AZbbnjCzI7qxWttf1OP6hsl2qD7Gk9lpVuu9NvGbcSHQomIiBh4F7gGuBc5snotXQtmV7W6cJ3CZfJCp31wRe1s+gUkoppWaZxE0ppZRSSguQ9FHgI9WvPV5NXROBPZO0KfAqGiezRhZKj/umg25vX3VffRs42vY/+x/S5EnankjAGti9XQJ3LNu/lLQ78FPgrZK+Z/vHAwy1a7ZnAZTWg58BFifiPZmoWrmVRsvaNYnK/z2BVxAngg8BjiHWo34e8Eai4mVRYHXgdGDTAd+M8Vphd9omeyrq5rY9Rkxu+TNwBdHyulbtoVvYB9iISBC9udNWtLYfKWtDzgGeAbwVOHpQQXbpRmAVYEtJS9h+uGnfG5p+/kWb8VWy7Z5BBJe6Z/t2YrLBVFZ1Mel1csBTyraW7+HjkbQkEf+idX9NbFXpLmnv8uPPbB845JCGYU3Gn9jSlm1LuojoirJGvwNLKaWUKpnETSmllFJKT5K0FfBRGtWe84CzgauJk7qPjS667kzQfvdfJT2nw6uaTZzkWZdGFdNPJhler8ab6T/dqwC6uX1VQmlOzStAKvuW7TW2u2qLXSoDryZabr6NqCislaaJIfcDu9o+t8Vh/yyX64ETJO1IrAt5NLByaYv7M+Bnko4hnoMrAJtI2sX2WYOKf7xW2BO1yZ7KpvNtK95EvJ5fYPvubgbavrO8x+xITCyoSxL3PKJ17XLEWuAHAUjahkjimqg4+3Wb8ZuUY/44+FAHS9KuwGuJFrx3AWfbPnW0Uc1YdxHrSq/d4/hNyvb2cY+qgdJi9w3EZKStaSSgF2qZL2llYLfy6822fzqsOLtQTeaZrhM7lizbXrspzBtzPSmllFLfZRI3pZRSSik1e0/Tz+cDe5T1vKaibWldRSZixny3s+arKtDbgC/0HlbvbF/Yy77pYJrfvs2Ix+o1PY7/DXGS+0V9i6hPyrraVWX/m9skcBdi+5xS7Xgm8J+SzrF9Zdl3laR3E9W8ALsQbaVT6sYGZXtrj+OrcRuMe9RwHQccBiwBHCBpFyL58lzi/I+Br7UaKOlZRMLTxMSt2pG0Eo2q1Ittf7jFMbOA7xFrFjfbU9J+wGtszxs7Lg3Ub4nlKLaUtKTthyYaUCldUJ5OPC4vG1B8fSFpI+BU4vkGE3cPuQd4L7AhcLukteu2tvEMmMxzN/F9YMMex1ev/11NBEoppZS6MWvUAaSUUkoppVrZumz/AbxhCidwKxpzaff3Ti7ziKTRlrbvHVL8aWZYvWxn9zi+alW5+rhHjcZ+xPPnOts/7Gag7e8TayiqXE+zU4h1IQG2mGyQaUZavmxX6XF8NW65PsTSF7b/Bryfxvvd2kSVfjWB/wbgiDbDd2v6+eKBBDh5ryA+p7wY+FWbYw4hJnbAwu/jLwGOHXCMfSPpuZIOlPRNST+SdGHZHl/+/tyJr6UWzinbZYmkZUckLcqCVe5n9zOofpL0TOBSIoFbPd4epFGpuZCy9vGXy7Gr0VifNQ3P74n//60lrd/NQElPIzofuFxPSimlNBBZiZtSSimllJqtQpyMON/23FEHM0lj2++KqC42UbXXSftLE+ty3gP8qZxwSyMgaZ3y4z97eWxKWp6StKnhunT3E2vFbt7j+CqJ2Ws7wEHamngetUu4TOQq4Fk0JpgAT65FdymRrOk1CZfGIamqoL7C9v/1MH57YCsA2//Vz9j65HZgPeBlkpax3fHzR9KywMuJx/YdExw+VLaPkXQHsTTC88ufHyIqWN9n+8GxYyTNBt5Rfn2caF1eRzuU7aPAQlX9khYHDqXRhePHROJvHeAA4nX23yR91vZVgw+3N5JeCnyK6NLQzpvLsVcCH7J9wTBi69EJREeGVYHDJc2xfcp4A0qr4ZOI9zcT7YZruTawpEWAM4gW/wAXAB+2fbmkLwDvHmf4acCR5ecdiM+ptSJpY2Ax4DHb13cx7pnE5LSHbd80qPgm6Rzi/30R4NuSdrA94drLkpYmHp9Vh4MfDTTKlFJKM1omcVNKKaWUUrO5wFOZBm3BWrXfjaXKALh1urXnlfSn8uO3yvqh3Y7/EPB2Ij9Wp/aglTnEibIvAQf2MP4/iAq1hdalq4HfEZMOni7pTROd3G4maQ8arSZ/N6D4JmPNsn2ix/FVa8k1W+yrOgUs1eN1p/EdTuM513USF3glcHC5jjomcX8OvIWoDvwy8O9djP1yGedyPbVS1og+qyQalgPusv34OEMWB3YvPz9k+x+DjrFHzy7b37VKRgM7EWuQGjjL9uurHZJ+A5xYft2LmCBSO5I+SVQTw8TteCEm//xM0hG2Dx1cZL2z/aCk9xIdFGYDJ0vah2g9/ORru6TnAM8gXjv2oPHa/gTwzqEG3Z09gY2Ix92ZwG6dtkW2fVv5/LY+sOngQuyNpHWJzxYikvH7dDH8UOJ19XFJ69n++wBCnKxjgQ8SEww2A34l6QPAD1vdh2XN452AzxCP1Woiz3FDizillNKMU7eTFymllFJKabRuJpK4q406kAGpkptXjDSKwViPOJm0Uo/jV2y6jumqkxPio/AdGpXjx0qik0SupN2Brzf96eR2x47Qw8CSNCoCu/W8sn2kxb6q/fR9PV53mtm+QiQYRKyXujLwXts3txsg6RnAUUSSCeL18suDDrRXZe3XCdd/LVXIU2Fi01OJ//N2VX07NP382TH7vgN8mvh8U8sW7JL+h0h8VUy06L0U+CtxXy5NVBZvAWxJo3XvwZKesP2hoQbdIdunSVqLSH7NArYvF2h87vhN05Dq/foJ4F01n3j3r2X7MLB/D+va/o5YW/XpfY2qP3Yj7q9qQk83vgzsTZx7fiPx2lkrtudJegeRfJ9F3A9nAPdIuhz4Cws+7zYnXocgHqOPA/u2mVSSUkop9UUmcVNKKaWUUrPvEGvNbSNpCdsPjzqgfuqlQjWlITiOWPP1hUTC82RJBxBVY5cDtxBr6y1F4+T9XmUr4uTq1cA3hx75xG4G/gXYRNI2ti/qdGBpKfpCSivNFoesVbYj6xwgqdcK44qJdtpzgeuBK4FTumlZWWNVEqaWE0NsXynpCKLq0URi9obSnrbd866qlKtu25G2rxxq4DPbymXbrlJ4m7K92/alzTtsz5f0K+A1RKKmViS9EDiMeCyKaAV9YAeTCo4mktcCPiDpNNu/HkLIXbP9OUm/JSZQbNi8q2zHTrT6A5EUPW8Y8U3Ci4jbcLHtu3oYf0/Z9joJb5CqCWZ/77YFeXmN/TsxcWI7apjEBbD9I0l7EZPiliEehysDO7Y4vPkx+gCRwD2nxXEppZRS32QSN6WUUkopNTseeA/RFu4jwIdHGk0apkXKdryWm1PZ4mX76EijaKEkF15LrIX3jPLnLei8WuyPwOt6qP4ZhjOIJK6A70raqZMEg6QXEa02K98bs38WjRPnf+lfuF2rkui9VnmLWKt5OSJR+ErgPySdDbyzpu0nO1WtD1nbCiXbh5XH0vtp3Ieb0X4t0ub7+ci6tq+dxhYr24XOZUlaEdiYkkxrM/72sl2u/6FN2v40Kh6/A+xle9wJELZvkvRqogvDm8r4dwH7DjjWntk+T9JGwM5EkmxLYA3idXAe0Zr26oznmQAAIABJREFUcmKN0dNr+r42VlWZOafH8dVtrOM52mcTj8leJwZUEyeePdGBo2T71DKB56NE9fHitH9ff4T4fPIx238eUogppZRmsDp+QEgppZRSSiNS1i3bFfgZcFipMvu47dolvlLfrVO29480isGpTiDeO9Io2ijr4m0JfI7u1uY8EXif7VreLqL94ruIx9eqwGWSTiKSFFfanlsdKGl5olXhHsQag9XEgltYuGXtdkQLcAOXDPIGdGBsxWknCd2xx44dsxNwlaStbI8ySd0TSbOBbcuvt44wlAnZPqQkzT9Bo7p9PJcB/8/2BQMPLo01l0iYrdVi30tpTKpoV/FfnQObbAX9ILy8bOcB+02UwK3YtqT9iETZ0jQqJ2ur3LYflMt08AgxwWD2RAe2sWrZ1nFpgCpBffu4R7VXjVulD7EMlO0/AXtLOhDYCtiEuP3LEFW3dxFdT35Z43XDU0opTUOZxE0ppZRSSk+SVCWPvkSjEvftkn4IXEu0MOz0xOIJAwmyj0rSaGvgBUTrtGWJSpaJ2PbbBhnbMEnaFHgVcd/eOOJw+kbSIsCaRFXFtsTtu3aUMY3H9n3AWyR9AngbcTL+eTSqzyAqiX8LXAAcZ7vW91fTxJCfEEnXRYk18vYGkPQgjZa1SzUNrRJp9wK7tlhv7uCm484aTPQTsz0LQNJ7iLUeFwd+SlTGXUkkMKv19NYkKjz3BF5BrJ94CHAM8X/zPGLdwH8n/p9WB06n0cJ3YJpe+1vZeIL9zWYTt3MXYH3iOXfZJMMbONs/B7aS9EziefcCFj55fw1wwTRpdT1V3Ugkg7ZsseTDG5p+/kWb8auV7T1t9o/S6sTz5QLbXU2msv1PSecDry3Xk4brDuLz40bdDpQkoluFiXWP66rX88eLjtnWXknQnlsuKaWU0shNmTfRlFJKKaU0FMezYJJWxAnTt3Z5PQZqm8SV9BTgU0QyZYker2ZkSdxysradf5X0nA6vqkq4rEujguknkwxv0sZZZ1TAuyW9u5erJW7f6T0HNiS2byLWRgSenGywDPDAVKz+sH21pBcDxxJrbjdXOi5dLq1cCry1VaLa9qv6HmiPJH2UmPRyP5FwbnXi95/lcj1wgqQdiWrko4GVy3rdPwN+JukY4nm4ArGW8C62B52oPp7WE3REVD1v1+P1zge+2uPYoSsJ2kzS1td5xLq3ywGfBA4CkLQNkcQ1cNs4Lds3Kcf8cfChdu0BYhJIrxWPdzRdTxquS4GnA5tKWr3LNvi7Ep+zTfvJB6N0F1H5vl6P46txI1u7PqWUUprqMombUkoppZTGatVKstf1HmtH0rrEibK16Lzt6djjOqpGHqBt28QgYm25Nbq8vur23QZ8ofew+ma8dUYn81i8ADhuEuNHoiRup1zytllJTG8jaSfgLUTr05VbHHoPcCHwTeCcTluKjoqkzYgELsCb2yRwF2L7nFLdeibwn5LOsX1l2XdVmahwcjl8F4ZTbdzuudXrc+5B4CDbl/c4PqWxjiMmuCwBHCBpF+I147nE+S0DX2s1UNKziLa1Jlqi1s2fgZVotNbtVjVuTl+iGYIy4Wxt4CnAolOhg0sbZ9HooPBp4M2dDJK0BjGRp3Ja/0ObtBuJ+2hLSSs0L4EwkTJhciviOfeHAcWXUkopTXuZxE0ppZRSSs2+NeoABqm0rTuTOCEF0Zb2ZGAHotLMRNXxskR16kuI9qcQ1S0foz7VBP1MuMwjTkJ+qEZrq/Zj4sDDxAn+a4kK3ONt13EtxBnD9tnA2QCS1iQSucsQj8G7bf9thOH1Yj/isfp72z/sZqDt70u6DnhmuZ4rm3afAhxJVGht0adYx9PqtX9vGi3WO0nEmgWfc+d22xY2pfHY/puk9wNfIR5va5dL9X5xA3BEm+G7Nf188cCC7N3pxOeNl0tatpvnjqTliDV1DZwxoPj6okykOxTYHVh+zO4Txhy7KpHkFHCV7U8PJcgu2T5L0m+A5wN7SJoHvL/FMgBPKt0YvkqjjfZ5NZ3w8lNge2JZh8Mp1e8d+lgZ53I9KaWUUuqBaj6xOaWUUkoppb6R9Abgu8QJpR8Dr7X9uKQvAO8m1rpdZMyYFxGVPZsAfwN2sH3DcCNfkKSXjv0TcD5xu85iwcqOdpoTLn+aAhWP84mYv2T7wFHHkxKApBuBDYETbb+lh/HHExVcN9veaMy+M4gq3Lm2V5x8tF3Hls+5VEulAvejRNIM4CFigtb7bN/V4vjZwF+INXEfA1apW2v6UrV4LZHUO9l2p+tQI+lEYnmIW4Hn12gy1gIk7U6sAb40LTqcjP38VcacR6xT/SCwmu1atouW9Dyiy8uy5U//AH4EPAPYnHgtPYhou/xK4n0D4v/hLmBT27VbE1fSKsCfgCXLnz5o+zMdjDuUaHkO8Vnz6bZvHUyUE8aywHOpueK7i/XeJzSFK8lTSinVXFbippRSSimlmeRfy9bA/rYfn2iA7V9J2pqoItgK+K6kzW0/PMA4J4rpwrF/iyJjAG5ttX+amDZtvdO0sWbZ9lrhPX/M9TS7s2yX6vG6J+sW4rXynhH9+wOVJ++nrrJG9FmSlibWx71rgvfzxYnKT4CH6pbABbB9n6TXA+cAe0paATjQ9px2YyQ9jZi0tRNwL/CGGidwXw+cVP0KzCXWkt2ASHS28w0iibsk8CrgewMMs2e2fyvptcCpRGvrFYjEOjSWvziqaUj1eebvwOvqmMAFsH2npE8TVbgGPlkmUXwRuMD2k2s4l8rplxOTIresrgI4YlQJ3OJ4GveBWbDiu3nfZIy93pRSSqlvshI3pZRSSinNGJJuBtYHfmV786a/t63EbTpmQ+B6YBbwdtu1WltV0kfLj1d0ui7nVNJUfXyr7Wm1tpqkfrV4tu2cqDtEku4m1nO82vamPYy/CnghcJ/tlcbsOxbYB7jD9ur9iDc1NFUaT1Y+79KkNU0qWA/4MDCbeHxeAlxGTKp4kJjUsQ6RJNuKSAY+CnyCqDYe1ygmHJSE9J+IxOZ84L+B/7X9yESfv0qi/l6iCOVrtvcfXuTdK4nMTwB7EJMH2nmcSGp/uDkRWkdlKZLTic4QY18zHyGWG1mGBW9vlaT+ge1dBh7kOMprfWWBx9mYfZPR9vtDSimlNFn5RSOllFJKKc0kq5Tt9WP+/uRJHElLtKqytf0HSb8EtgHeBNQqiWv7Y6OOYZCmcXUxxMlOk5XGU9HNwL8Am0jaxvZFnQ4sExNeSNz3N7c4ZK2yrcs63NNRt8+5fJ6mQTmehRNks4Cty6WV6r2jWq90IqOqFnwHkcA18DHbH+90oO15Ze3w59Non11btu8A3ibpMGIt2S2BNYj1f+cBdxBrjP/Y9m0jC7QLtl2WI/kE8AHicVlZolzGmk+sT/3hwUc4oVZrvneyL6WUUqqFTOKmlFJKKaWZpDrRNHZNtebfVwTanVi7mUjibtRmf0q96iUxVJ3wnxJJpdKWfHdgM6LabHk6/05a12rHM4gkrohW6zvZ/vVEg8pa26c2/el7Y/bPAl5E3McTVtelnnR68n4W8Vh9LvC08reHgdNomgCUUh+0ei2f6PV9Krz+71i2dwOf6mH8TUQCd/2+RdRHkparfrb9z7K9GzilXKY82/OBD5YOEe8n1vVdr8Whc4BzgaNst5qcNHS29+llX0oppVQXdfwSnFJKKaWUakTSMsTJs5WBZVlwBn5bNV0j8J9EknbsGpPNaz5uQPsk7vJlu0qb/Sl1zXZHz6nS0rBKJr0R2JdIIu1X0+cbAJJWJKq/Xj121wjC6bcvAe8i2puuClwm6STgO8CVtudWB0paHticaLO5J1C1XrwF+PKY692OeK2q2qkOhKR1mn+3fUu7fZPRfL110cvJe0mbAp8nqutWJ9Yg/We/Y0sz0nSuCNyIeC272PajPYy/r2yXH/eo0ZlL3L47JK1tu19LJNROSczuDyBpFeJ9b1ngfqL1/53jDE8ppZRSDzKJm1JKKaWUWpK0B3AAUTXXS8vJOiaV/kgkRlYb8/ffN/28LbBQS9RSGffC8uuDgwiun0rCaGvgBXSXgLfttw0ytnYknT8mju3a7JuMBa53KrFt4mTxRcBFpSLmx8A3JS1p+5iRBtiCpEWBnwCbMD2Stguw/aCkXYnbuCLxHXvvckHSgzTWsWyePFL9X9wL7Gp77GvKwU3HnTWY6IGomqoqus2C5wia903G2OudsmxfJeklwNnAK4j3uZGu95imh2leEbhi2faa4FusbOta+f4E8fnqoumcwB2rJGwzaZtSSikN2LT4IpVSSimllPpH0pLAd2m0vxsv8TLV1ge8hqiEe9aYv18GPEKcKNxf0ldKK7xmBxGtNA38btCB9krSU4h2hXvSep2yTowkiUsk0KvH1Njk0bYt/tatVtc7Zdm+WtLeRELpKEkX2r5h1HGN8U4a675CtA0+DrgauMf246MKrF/K/fBi4FjgxSz4mrh0ubRyKfBW2ze2uM5X9T3Q8Y33Oj6VXuMHzvYTkvYF/gDsLGlX22eMOq6UauwfwErEZLJeVF0B7hn3qNH5/+3dd7hkZZXo/+8CmpwkiJIREdRraFGSoARBR/wRlIsCjjAEw4AMmFCfGRnudXAGdfAHOooBiYIBwTsiggoDiijYMOPgRUSgCY00QTI0qdf9493lqS5OqKpTYZ9T38/znGfXqb3fXatOxbPXftdaSJmZ/8BUG6peIuJT1cWrM/PHXYx/E7AdQGb+r17GJklSg0lcSZIktfo6sHt1eRFwGSV5uQVjM2xXATYCXgnMqa5/jNIfss5Jsv8A3gusHxEvysxbADLzoYg4j1LmdB1gXkScSJmhuyKwJ/Cepv3UssdZRGwEXAGsT3uJl/GS8MN+/EwmdSAzL4qI31PKVX4A+Lshh9Rq36bLR2XmSUOLpI8y8w/ADhGxO3AQ8EbKDPhW9wOXA98EflTNrh6m25n4NT/ZupGWmXdGxJXATsDfUD77JI1vAeX98NWdDoyIFYFtKO9Ff+hxXL1yMyWJu+6wAxmk6qTP5wHL1LFkfpv+kfLc+hLQcRKX0hv4w9U+TOJKkvrCJK4kSZL+IiK2BvajHIz4I7BbZt4WESdTkrhLlPyLiFWBw4BPAStTesW+MzMfGXTsbfoR8BQl8bwPcELTumMoPTtXpyRBPz/BPq6lJLprpeqXej6wQXXVb4Gzgd0o/TUTOJixBPwbKKWyAR4FjgNaZx8P2k5drht1v6G8PncbdiDjeDnluXfjbE3gNsvMCykzo4mI9SiJi5UpJ7ncl5l3DjG858jMjbtZJ6Akbnai9IyXBqYq4b4H5aSze4ELM/Pbw41qUpdRXicvjYi5mXldB2OPoJxMl0Cv2ir02veBHYA3RMSK45THnxWq75n7UCq9bE9J4MI4JfMjYi3GTuK6KTN/Mqg4JUmabUziSpIkqdmBTZcPzszbJts4Mx8GPh8R/4dykO7NlBlm+/QvxO5l5sMRsR8l2bygZd2CqizaecDGE+zickqS+um+Btqdd1BmuSSlT+oemflMRGxISeKSmac3D4iILYGvUvqV/h0laT+0cryZeXk368Siarn+UKMYX6OU8K+GGsUQZOYCWt5nNKs0ytU/f6hRaFaIiDUpJ2IB/CIzPznONktRStLv2bLqgIh4P/C2zHysv5F25VuUlhQAp0TEju0kOiNiF8ZmNz4DnNWn+KbrNMpszPWAz1C/ihjTFhGbA98GXtG4aooh91P+Di8G7o6IDTKzrj2Np6Pxd7BqhSSpb5YadgCSJEmqlddXy5sz88p2B2XmTZQEcAB7VyVFaykzz8/MUzLzh+Osu44yo/EASt/OH1Nm734ReEtm7pSZ9ww04PbtXS0T+EA7vUYzcx5lNsUvKQnA70REt310NTxbVMs6HiC9q1rWMTapK1Uy7Q3Vrw8NMxbNGrtSPo9fD8ybYJuPAntVl6Pl5w2Uvty1k5m/ocxWDWBL4IqI2Hai7SNi3Yg4gfL9a1nK95qv17Vkb2Y+BLwLeBg4IiK+EhGrDTmsnomIl1J6uL+Csefb45QKE+OqWgX8W7XtC4Ad+x7ocKxeLWfl7GtJUj04E1eSJEnN1qMcLGstdfeXM8wjYtnMfKp1YGb+LCJ+B7wMeDdVSdGZprpv51Q/M8lWlMfp2qlmUDfLzCci4iDgBkrp2/0pCWzNABHxespB/wRuHXI447masZ7a0mzxaWBDqvfcIcei2aFRDv8p4KLWlRGxHPAxxr6PXUz5nrUh8EFgOeB/RsTnqqRp3RxK+X64BaX6xy8iYgFNMzoj4jxgM8p3EZrWXQd8aHChdiYi3lNd/CKlNcdhwF9HxCWUhPy9wBPt7Cszz+hLkF2KiKUpCfhGsvIy4JOZ+euq1crhkwz/LvCv1eXdqG857K5ExBzGktNW3ZAk9Y1JXEmSJDVbtVre33J988GnVcZZ33At5eDblj2OS1NrlPS8oeX6v8yAjIjlM3NRy3oy848R8UtKT7d3UcMkbkTcUl08PTOP62L8JygHVjMzN+1pcEMQEStSZoz/M+VAdwL/PtSgxvdV4J3A1hGxWTVrXxq6qtR8u5YB1qSUrD8QaJ5FePq4I6TONBKX109Qanh3Sg/SBC7IzHc0VkTEfwFnVr++m9InvVYy88GI2IlygtyO1dXrNVZXy+ZZxg2XAvtm5pN9D7J7p7FkOd0AVqD0Ld6jg/0kUKskLuV7xuaU2M6nPBZtVdbIzLuq724vAl7bvxDb05RsH88WU6xvNofy3N2Lct+SEWwZIUkaHJO4kiRJavY4JUk7p+X6B5sub8TESdzGgbcX9jguTa1RBvnRluubf1+DsfK2rW6iJHE373FcvbIx5UDZml2OX6NpH7USEZ3MTmkkk17Mkv/P3cfYjJfayMzLIuJrlAT6GRGxSzu9EGeiiNge2A94HeW5thrt/8+dmen/54M1n+m/H/woM8/tQSzS2pTn4x8mWL9b0+XPtaw7BziBUrZ2m96H1huZubDqc/tu4GjKSRETuQH4F+CsGdJLdbwesVP1jZ0JGq06FlFadXT6WFwPbEqZYT1spzH+e34Au1Q/3VgMfKXLsZIkTcl/EiVJktTsDkq5u9ZEWfNBxW2ZuHzky/oRVCc6nF3VtRr2ZnuYkqhcseX65oT7pkycxG30b3v+BOvVPzvSeTKp+eDwXcD/l5kP9Cyi3jqCcmLIQcC8iPg4cGE7fZtngohYgzJ76q9aVw0hnJ6JiGenuYsEHqGcBHQDcA1wbma2VgsYtm4ep8XAl4EP9zgWja61quVEPZZ3qJb3ZeZVzSsyc3FEzAPeRvmcr62qV+qZwJkR8QLKd8p1Kd9BHgMWAr/KzPlDC7Jzs3k2/paU9/JfZOa9XYxvfAft9gS8Xpvo/b7bz+vHgaMy89ddjpckaUomcSVJktTsvykl/VpnY17NWJLp0Ij4SmYucYA/InYDXlNtdwvDM5/+z7ZM6vdd+mZKEvcFLdf/runyjsDPWwdGxFKUxw7KAanZaOlqWdfEYacHEBO4EfgWcFJmPtz7kKavZZbxk5T3lu8DiyLiRkrCop3Xa2Zmt7Nk+iYilgEuofR4nNFJ23E0ynR3e7+CkphZldK3883A30fEhcD7MvNPPYmye7fT/mfF05SE9K2Uz8Nza3gij2a2Zavlc75bVCeKbEGVTJtg/N3VctUJ1tdOZt5NKdE7o2Xm3ww7hj5au1rO73J8Y+ZuHb4zj5dsP5Cx71PtJGKTMiv5fsr/TBdl5iM9i1CSpHHU4UNUkiRJ9XEFpSfq5hGxRmb+GSAz74iIX1BmgrwSOD8iPkVJEK4I7MmSpVzr0JtztiVUpvKfwFY8dzb0ryjJs2WBD0TElzPzvpZtjgI2oRycur7fgQ5JY4Z2HQ+27dTBto1k0vwZcuBwR5ZMlDWSgisAr2pzH41kYh29j7GTVwC+R+kpfR1w/yyYbdx4H82W3yfTum3rmN2B30TEdpl52zTj61pmbjys25bG8SAlYbb+OOveyNj74HNOxKo0ju9Ndwa91Kzx/bG1zUq71qmWQ68UMl6yPSIOrC7+NDOPHHBIkiS1xSSuJEmSmv2IsSTL2yglQhs+ztgMkN2rn/HcB3yhXwG2oZPZVbPJfwDvBdaPiBdl5i0AmflQRJwH7E85mDYvIk5kyQT8e5r2M+v6O0bEa4G3MDbbolYy8/Jhx9BnvS5fWCf7Nl0+KjNPGlokPZaZSwFExBHAZ4HlgJ8AZ1NKIy+glD9dCViP0gv4AGBXykyljwKnUCoEvBJ4J+W9ZhlK3/TzgNcO7A5J9XYjpZ3BthGxfGYualq3T9PlKyYY36jCcf8E66VuLARW4bkVeqYUEQFsTfnudUeP4+qVxv8Mvm4kSbVlEleSJEl/kZm3R8S/UmaCrN2y7qqIOAz4ChN/j7wX2HOcmZ4DM8Kzq34EPEWZLbEPcELTumMo/TpXpzy2n59gH9cCX+9jjG1pKcHbau+I+B9t7moOJbm0EWOzmC6ZZnjqTCezjGeil1OdHDCbErgNEXEs8CnK7O+3Z+ZF42z2cPVzA3BGRLwVOAc4CVgrM48Dfgr8NCJOobwGVwfmRsRemXnBAO6KVHc/o1Q7WRX4Z0qFDCJiB8pnegJ3Zea1E4yfW21zc/9Dnb6IeAXl82Eu5fvmKpT3mfso30Uuy8z/Hl6EqlwFbAa8NiJe2GEZ/LdTTkxIJj75YKhG+H8GSdIMEpmjOElBkiRJ3YqILYAPATsD61L6Xd0C/BA4MTPvHWJ4Iy0i9qYcMFuQmT9sWTeXMvNt4wmGXw68MzPv6WuQbYiIxTx3NnVrWdeOdlktFwCvapQJr4uIaJR6fjgzH+xi/GqU3qPYp3OwIuIJSqnJ0zPz4GHH00sR8TpKOXaAvTKz7TL5EbEnpdflYmDbzLymad1+lNm8CZyZmQf1LOgOVC0BAK7OzB93Mf5NwHYAmfm/ehmbRk9ErE+Zjbt8ddUdlNmBr6CcOJfAP2bm/x5n7MsorRCS0iP96IEE3YWIeCPwL5SZ+1O5BvhEZl7W36g0kYjYi9LHPoFvZeZfN607GTic0rN+6ZZx61IevxdWY7fLzHZ6zkqSpBYmcSVJkqQRERHLAu8AdqElAZ+ZtZmhWiVxe+kx4ALKweA7e7zvaWtKWn+pm55sEfFZyokVmZlWWxqgiLiZcmLENzPz0CGH01MR8Q3gb4DfZeYruhh/PfBS4LTMPKTp+gDuopxwclNmbtGjkDuNrxevuw8zTgJD6kZEvA/4MkuerNQ4CekG4HWZ+fg44/6RMmM+gX0z87w+h9qViPhnSpl1aL+cfgKfz8yP9Seq9kTEG5p/z8wrJlo3Hc37rYuIuI7Swz6BrwEfyszHJ0riVtUYvkKp/JLAzzJzt8FH3p7q5NRlgacz84YOxr2UUvFlUWb+oV/xSZLkP/iSJEnSiMjMpyhlTs8ZdixTaC3BG8CllIOBF1DKtE4lKX057wduydl/9ups6C87E10NbAIMJRHZZ9tTXkfzuhz/G+Bl1X7+IjMzIq4C9qIkciUBmXlKRCwEjqUkzQCeoMxqP3qCBO4c4L3Vr89QSpfXTkT8E9CciE1Kqd6rKLOOG/21NwS2AbalfK4F8OGIeDYzPzHQoJf0H4wl15Mlj6c2r5uO1v3WxYGUcsirAIcB+0bED4GXNDaIiA9Syi6/GXhx09j7gEOoqYjYiDKLPYAzKCcutetjlD7vz0TExh2WmpYkqW11/HIgSZIkaYRl5uWt15XJe0ApFf2c9dKQfBV4J7B1RGyWmTcNO6AeWq9aPtvl+MaM+vXGWdco275il/uug+mUeJfGVfWIviAiVqL0x703M5+ZZMhywH7V5Scy86F+x9ipiHgNcAzltRLAxcCRk71fRsRLKCds7VaN+UhEfHeSnsCDMNnJUrP2RKrM/G1E7AF8G1iH0tP8gMbqavmFpiGNv8WfgD0z846BBNqdfYGlqKoydDj23ygJ7mUo3wO+MPnmkiR1xySuJEmSpJnguGp59VCjqKflquVTQ42iTRGxK2W29WuAtSizex6hzNi5Frg0M2s5m6xVZl4WEV+jzE46IyJ2GW+23Ay1CFiBsRmBnXpltXxynHVzquUDXe67DlavlrPl8VaNZOZjlNmpU233KKWnfZ19gLFE2TnAu6eqjpGZf4iIv6L0z35XNf5vgWGVrb+CiU/YmGzdrJCZV0TEq4Hjgf0Z+94xnmeAs4BPZubdg4hvGhqVX/6Umb/pZGBmXhMRfwJeQGlTYhJXktQXJnElSZKkGSIiNhzE7WTm7YO4nU5k5nFTbzWyXl4t/zzUKKYQEXsD/wJsOslmuwLHVL1mP1bNSqu7IyhJyYOAeRHxceDCKWbPzQQ3AVsDcyNih8z8ebsDI+KNlCR9VvtptX61vG/aUQ5BVcJ2x+rXBUMMRZoJdq6WjwHvb7e9QVV6/f3A2yillltbLQxMZu7YzbrZJDMXAodExDHAmyglr9cFVqM8tguBXwMXZ+ZdQwu0My+nfE51O8N7HuX5+fKpNpQkqVsmcSVJkqSZYz79n+1R155sahIRS1PK1O5LSSYl8N/DjGkyEfF54KjGr20MeTFwXkScmJkf6V9k0xMRlzb9+iSwOfB9YFFE3Ag8RHuv2czMXfoQ4nR8n5LEDeA7EbF7O6VMI2JLStnNhu+1rF8K2JLyd7mtd+FOGtN7Jlm9xRTrm82hvO72Al5EuQ+/mmZ40mz3Qspr5bLMfKSTgZn5cPU+u0e1Hw1QRKzauJyZD1fL+4Bzq5+Zbu1q2e2M4cY4+7tLkvrGgzOSJEnSzDNre6/NdhExUX/RAA6PiMO72S3lAPl5XQfWR9WsnaMZ64f4NPBj4ErKiQmPUWZZbQS8HngLsGy17dERcU9mnjD4yNuyI0smaRv3sZMyxI3Hr26+RClfuiGlD+KvIuIsSjnUazLzwcaGEbEasBWlzOYBwNLVqtspfQOb7QKGTFnnAAAgAElEQVSsQbnPV/bzDjQ5jfH/xlHF020CfTHwlS7HSqPiUUr53W4TZQub9qPBepDy3rkwIjbIzG57pNddt8fHl2lZSpLUc37ISJIkSTPH7dQz2TNwVdJoe+DVjPVVXaqNoZmZh/Qztik0EnbjJeKnk5y/DDh1GuP7IiLWB45l7D7/ADh8klKLn42IdYCTgX2qMcdGxLcy885BxNyFiR63GX2yRWY+HhFvBy6hJF2XAQ6sfoiIxyn9YFesfhoa9/vPwNvH6RH84abtBlkuu9eP0+PAUZn56y7HS6PiVmBNyskg3WiMm9+TaNSJZynfrX4+SxO491LK+2/c5fjGuBnZGkCSNDOYxJUkSZJmiMzceNgxDFtEPI/SV/UAYPkudzPMJC70Jrm3CLifUkL5POC0mh5gPZTyOCVwZmYeONWAqu/evhFxKqXP7PKUx6yOfZGH1qNxEDLzuoh4PfANyizp5ufuStXPeK4CDs7MG8fZ51t6HujUTh/nugMpz8sbKX0cp5Is+bq7qNPSsNKIOg94HbBzRKzSyeumKue7M+X19/0+xddOHBsO4nYy8/ZB3E4HFlLKWD8w7ED65EZgA2DbiFi9ucLEVKrvo9tRnpt/7FN8kiQRmZ7IL0mSJKn+ImIj4ArKrIl2EqHjzXjNzFx6vI2HJSIWU2L9UmYeOex4eikirqL0VX0EWC8z2y6HGRErAwuAlYGrM3Pb/kSpdkTE7pSk+hsps99b3Q9cDnwT+FHW/GDDbH7dSXVSJbv+m5IMPDsz2+1BTUScSTlpawHwqsz8c3+inDKOxvtFP2Vm1mqyTURcTql6cmFm7jHseHotIj5KOTEwgZMz86gOxp4EHFGN/VRm/lN/opQkjbpafTmQJEmSpPFERADnU2ZMAPwWOBvYjdLPMoGDKWWVNwLeQJn5A6WP3nHUu9zdjC69O4lNKI/NZZ0kcAEy89GIuBTYE3hRP4JT+zLzQuBCgIhYj5LIXZnS0/i+Gpe7nkijPP39ww5Ems0y84GIeAfwI+CAiFgdODIz5080JiI2AU4CdqeUZt9nWAncFrP1s3oi3wd2AN4QESuOUx5/pjud0vJhBeCDEbEgMz871aCI+BglgQvwJKXvuiRJfeFMXEmSJEm1FxH7AN+hJF0uBvbIzGci4mTgcMaZYRsRWwJfBeYCdwK7ZebvBxv51CLijdXFBZk5q0ryRcSTlJOHv5aZ7+9i/CnAYcDTmblcr+OTJPVXRDRm3m4MfBKYQ/ksvxL4FeWEikZ/7Q2BbSllagN4CjgeuG2q28nMM3oc+l9ExHzam4m7LuUzr5HsTeAhyskuKwGrtax7GvhTY3BmbtKbiHsjIlajzKJeD/hiZv7dkEPquYj4FPCPjD2+vwK+SDn57O6m7dahlPY+nPIcjWrM8Zn5D4OMWZI0WkziSpIkSaq9iDgb2A9YDGyambdV10+YxK3WrwD8hHJA+Hpgq8xcNLDAR1xE3A2sDfx7Zu7VxfgLgD2AezNznV7HJ0nqrwlKEcc413WyvtVQSxFHxBxKWd6jKN9TzgbOoLQCeKRpu1WArSil6fej3M8vAMdk5jMDDrstEbEdpQrDqsDXKLE+NNyoeqeq9HIesBfPfc49SanmsjLQfCJZIxH/f7r5biNJUidM4kqSJEmqvYi4iVJSd15mbtV0/aRJ3GqbFwM3AEsBh2XmqQMIWSzRE/dRYN0Oe+KuBNxFOXh6TWZu058oeysidgV2Al5DKTm8CqUn8H3AtcClmfnT4UUoSYNTJXH7baj97quqEYdS3uf3ysyr2hizHXABsCZwamYe1t8oO9c0i3oz4BhgaWARcAkwD7gXeKKdffVzpvR0RcRSlBnfH6F8V5zKYuDzwCcz89l+xiZJkklcSZIkSbUXEQ9RknlnZeaBTdf//8AHKbMnVppolm1EXE7p6/bTzNxtACELiIjjgH+gPD6nZ+bBHYz9KuWgeAKfzsxj+xNlb0TE3pSZWJu2sfnNwMcy84L+RjXaImLD5t8z8/aJ1k1H834lLSkivjmI28nMvxnE7bSKiDdRkpoJ7JqZl3YwdhdKtZAE3pqZF/cnyu6MM4u6uRR0J4Y6U7pdEbEZ8CHgzZTy363mAxcBX8jMmwYXmSRplJnElSRJklR7Tb1Vv5KZhzdd/0/AJygHFDfIzLsmGP914GDgjszcaAAht95+80HdzMxdJlg3HUvstw4iYgPgRsbKEH4PODIzF04yZi1KeclGqclFwOaZeUefw+1aRHyeUkYTxg5yTyWBEzPzI/2JqnciYnvK4/E6yoHt1Sivx3YM7eB9SwJiiTgmKPHajRmRnJDUHxFxLrAvcF1mbtnF+HnAq4HzMnPfXsc3HT2cRT3UmdLdiIjnA+swVk1jYWbeM9yoJEmjyH80JEmSJM0EDwNrACu2XH9/0+VNKeV3x7NatXx+j+Nq146UhNF4ff52HOe6TnXaP3AgMvOOiPg08GlKfPsAe0TERcCVwG3AY8BKwIaU3sVvpSR9G/fp0zVP4B4DHM3Y4/s08GPK/ZvP2P3bCHg98BZg2WrboyPinsw8YfCRTy0i1qD0dfyr1lVDCGc6Jot3pt0XSfXyOsr7/392Of6/gLlAxwngATh92AEMS5WwNWkrSRo6k7iSJEmSZoKbKUncF7Rc/7umyzsCP28dWPU6e0316+P9CK5NI5lIyszjI2Jd4G8pB7qXA/asfsbT/Lf4UmYe3+cQuxYR6wPHMpbA/QFw+EQzwoHPRsQ6wMmUhHYAx0bEtzLzzkHE3K6IWIZSInQuM/f5eTsTn9ww2TpJatcLq+WcLsc3Zqi+cNKthmBYJaolSdIYk7iSJEmSZoL/BLYCXtZy/a+AJykzGz8QEV/OzPtatjkK2ISSsLm+34FOYKcu180KmXlERFwBfIbyWEyVFLwV+HhmfrfvwU3PocDylOfWmc39midSlZLeNyJOBQ6qxh8CHNfHOLvxPsrJD41E5/eAU4HrgPsz85lhBdauzNy4m3WShiMi3g7sQSljey9wYWZ+e7hRTekRyslJW3U5fptq+WhvwlEvRMQKwPOAZex7LkkaJnviSpIkSaq9iHgX8C1KQmmzzLylad1ZwP7VujuBEykzdFekzPZ8D7BUtf6IzPzyYKNXQ0QEsBuwM2WG59rAypSD1/dSEoSXApfkDPhnNSKuAramHMRfLzPbPggfESsDCyj3/+rM3LY/UXYnIi4HdqC8bo7OzJOGHJKkGSgi1gTOr379RWZ+cpxtlqKcKDJehYYrgLdl5mP9i7J7EfEzyslYCRyQmed2MHZ/4Kxq7OWZuXN/otRUqu8n+wAHANtTErgwTt/ziFiL0gcZ4KbM/MnAApUkjRyTuJIkSZJqLyJWpfQmmwN8ormHaESsB/wWWH2yXQDzgO0y8+l+xqrRERF3UxLR/56Ze3Ux/nxK0uLezFyn1/FNR0TcRzmIfWNmts6Al6S2tJyEtW9mnjfONsdQKjU0StM3S+C7mfmufsfajYg4FPgqJc5FwCHtJHIjYj/g68AK1dj3ZuY3+hmrxhcRmwPfBl7RuKppdWbm0i3bB/B74MXA3cAGmbl4ELFKkkbPUsMOQJIkSZKmkpkPA/tR+qr+35Z1C4A3AbdRDryN93M5sLsJXPVYY6bO3V2Ov6daTnYCwrCsVC1/NdQoJM10u1XLp4CLWldGxHLAxxgr3X4xcCTwOUq7hAD+Z0S8tv+hduVU4FpKnCsAZ0fElRHx/oiYGxFrRsQK1XJuRHwgIq6kzMBtJHCvA745tHswwiLipcBVlARu4zvj48CEM7+rSiH/Vm37AmDHvgcqSRpZ9sSVJEmSNCNk5vmTrLsuIrYA3gHsAqwLLAZuAX6YmZcMJsrORUSjNPTpmdlxX9SI+ARwGOW44qY9DU5TeYAyE/cFXY5vzL59sDfh9NRdwMaU15Ekdevl1fL6zHx8nPW7U06ISeCCzHxHY0VE/BdwZvXru4Hf9DPQbmTm4ojYg9IK4CXV1dsw1ut2KjcDew5rJmdEvKH598y8YqJ109G837qIiKWB7zN2ItVlwCcz89cRcTJw+CTDvwv8a3V5N8rjL0lSz5nElSRJkjQrZOZTwDnVz0yyMeXg9Zpdjl+jaR8DFxEbDuJ2MvP2QdxOh24Fng/sHBErd9gTdyXG+ije2qf4puNqYBNgi2EHImlGW5vyPveHCdbv1nT5cy3rzgFOoJwo025SdOAy866I2BY4EXhPB0PPpPQc/3N/ImvLfzD2/SFZ8lhx87rpaN1vXRwAbE6J73xKue+2kunVY34L8CKgrrPEJUmzQB0/QCVJkiRJM8d8+p9ArusB4EuArSmlh08CDu5g7InAKpT7dnHvQ5u2rwLvBLaOiM0y86ZhB9RLEfHsNHeRwCOUWdQ3ANcA52bmDdONTZpl1qqWD02wfodqeV9mXtW8oprlOg94G1DrShOZ+QBwUEQcDxxCOUnnlcCyTZs9BfyWMuPz1My8ceCBjq+1D3G762a6vavlIuADXcyGvp7yvNysp1FJktSkjv8ES5IkSZLat3S1fGaoUczuA70T+TrwUWA54MBqdu2RmblwogERsRbwBUqPZyg9H7/e70A7lZmXRcTXKKW6z4iIXSYohTpTBSUR2+3zNoDVgFWBDYE3A38fERcC78vMP/UkSmnmayQxn3MMMiLWoMz2T+AXE4xv9Bxftfeh9V5m/gE4pvF7RKwGrAw8mpkTJbKH6QomPhFrsnWzwZZUz73MvLeL8fdXy24rqUiSNCWTuJIkSZJqYcTL8k5H4+/2yJBu/3Zm90HeCWXmHRHxaeDTlL/BPsAeEXERcCVwG/AYZabuhsB2wFspSd9GEvHTmXnHEMJvxxHAHOAgYF5EfBy4MDOHfcJArzQSuNny+2Rat20dszvwm4jYLjNvm2Z80mzwIKWk8vrjrHsjY++FP59gfOPY5XRnzw9FlbitY/IWgMzcsZt1s8Ta1XJ+l+MbM3c9vi5J6hs/ZCRJkiTVxXxGtyxvVyLitcBbKPdrKGUZM3PjYdxuXWTm8RGxLvC3lMdhOWDP6mc8zUm/L2Xm8X0OsSsRcWnTr09S+gZ+H1gUETdSkhLtvF4zM3fpQ4jTkplLAUTEEcBnKY/bT4CzKaWRFzCWgF8PeB2lf+KulNKbHwVOofSkfiWl9PR7KO8vLwTOwz6JEpTPpucD20bE8pm5qGndPk2Xr5hg/Auq5f0TrJe69SRlpvicLsevUy0f6E04kiQ916w5eCFJkiRp1pi1ZXlbEmOt9o6I/9HmruZQEksbMTaL6ZJphqcuZeYREXEF8BlgE6Z+Dt8KfDwzv9v34Lq3I0smaRulh1cAXtXmPhrPzVqKiGOBT1Fmsb89My8aZ7OHq58bKGWl3wqcQ+mBvFZmHgf8FPhpRJxCeR2uDsyNiL0y84IB3BWpzn5G6Xu7KvDPwFEAEbEDJYmbwF2Zee0E4+dW29zc/1A1YhZSetNv3unAiAhga8pzs67VNCRJs0Bk1vb/KUmSJEkjJCLmM4CET2Zu0u/bmEhELOa597G1pGtHu6yWC4BXZeafu41N01cd1N0N2JmSeFibqhcicC9wHXApcEnW/J/x6rnaC5mZS0+92WBFxOuAX1W/7pWZ/97B2D2B8ymlNLfNzGua1u1Hmc2bwJmZeVDPgpZmoIhYnzIbd/nqqjsos2pfQZlcksA/Zub/Hmfsy4Drq21OysyjBxK0RkJEnEapoPA0sHFzL/OIOBk4nAk+wyLiHcB3Kc/NEzPzIwMJWpI0cpyJK0mSJKkWRqgs70SzNLuZgfwYcAHwCRO4w1clZi+ufma6nYYdQJ+9n/Ka+10nCVyAzPxBRPxf4KXVfq5pWn0u8K+U8rHb9ChWacbKzDsj4kPAlykJrw2qn8Zn3u+Bz08wfN+my7/oW5AjLCI2HMTtZObtg7idDl3AWBn8E4C/bmdQ1ULhpKar6lxVQ5I0w5nElSRJkqTBaU2MBWVmZlIOJp70nBHPlZSenPcDt9R9Rqdmpsy8fNgx9Nn2lNfSvC7H/wZ4WbWfv8jMjIirgL0oiVxp5GXmKRGxEDiWsXLsT1BmtB+dmY+3jomIOcB7q1+foZQtV+/Np/9VUJIaHoPOzAsi4r8oz8n9I+Ix4EPjPR8bqpL6X6H0Pk/gZ5n564EELEkaSbX7AJUkSZKk2Wq8xFipwAvAghFInEl1sV61fLbL8Y1y0+uNs+6earlil/uWZp2qP/QFEbESpT/uvZn5zCRDlgP2qy4/kZkP9TvGEddNNZDZ4EDgCkpv3MOAfSPih8BLGhtExAeBzYA3Ay9uGnsfcMjgQpUkjSKTuJIkSZI0XMdVy6uHGoXGNeKlJmezRcAKjM0K7NQrq+WT46ybUy0f6HLf0qyVmY9RWgFMtd2jgCc29d/ttDcTd13KceRGsjeBhyiP5UrAai3rngb+RM1l5m8jYg/g28A6wOrAAY3V1fILTUMa9/FPwJ6ZecdAApUkjayw8pYkSZIkSeOLiMUMoNRkZnqS9QBVJY+3pjy2O2bmzzsY+0bgsmrsNZm5Tcv6i4FdKf12X9G7qCVpsKqy1v8CHEWpQHA2cAZwdWY+0rTdKsBWwEGUGdRBSX4eM8WM61qIiHWA44H9KbPAJ/IMcBbwycy8exCxSZJGm0lcSZIkSZIm0JTE7WepyczMpfu4/56IiF0pfZ1fA6xFKT/5CKWk5LXApZk5I/pWRsRHKYkJgIXA7pl5bRvjtgQupPS7TUqC4nNN65eilFN+HnBRZr6t17FL0qBExCnAoZT3+b0y86o2xmwHXACsCZyamYf1N8reiYi1gDcB21JmH69GmW28EPg1cHFm3jW8CCVJo8YkriRJkiRJE4iI+fR/Ji6ZuUm/b6NbEbE3JeG5aRub3wx8rOp/WVsRsSLwO2BDSoK+MbvqHMrs2gebtl2NMsNsf0qZzaWrMbcBL8/Mx5u23RW4mPKc+fvM/MxA7pAk9VhEvAm4hPJ+tmtmXtrB2F2An1Rj35qZF/cnyu5FxKqNy5n58DBjkSRpIiZxJUmSJKlGqoTR9sCrGZvtuFQbQzMzD+lnbBo9EfF5ShlNaH82cgInZuZH+hNVb0TEXEqCYg3KfWs+QPJ49bNi9fOXYdXyz5SkxnUt+/wxsFv168sz84Y+hC5JfRcR5wL7Atdl5pZdjJ9H+S5zXmbu2+v4pqup0sZCYIPMfHbIIUmS9Bz23JEkSZKkGoiI51FmOx4ALN/lbkziqmci4hjgaMbKST8N/Bi4EphPKTG5ErAR8HrgLcCy1bZHR8Q9mXnC4CNvT2ZeFxGvB75Bib85Sb1S9TOeq4CDM/PGcfb5lp4HKknD8TrK+/9/djn+v4C5QMcJ4AF5lnKS3M9N4EqS6sokriRJkiQNWURsBFwBrE97sx3H69FqmSX1TESsDxzL2HPtB8Dhk/QC/GxErAOcDOxTjTk2Ir6VmXcOIuZuZOYfgB0iYnfgIOCNlBnwre4HLge+CfwoLWsmafZ7YbWc0+X4Rq/3F0661fAspMT2wLADkSRpIiZxJUmSJGmIIiKA84ENqqt+C5xNKcm6CyWJdjClrPJGwBsos2MAHgWOA+4bYMgaDYdSZoQncGZmHjjVgMxcCOwbEadSEqLLU2aHH9fHOHsiMy8ELgSIiPUoidyVKbON76tzIlqS+uQRYDlKT/BubFMtH+1NOD13MyWJu+6wA5EkaSL2xJUkSZKkIYqIfYDvUJJlFwN7ZOYzEXEycDil1+3SLWO2BL5KKVN4J7BbZv5+sJFrNouIq4CtKQfx18vMtg/CR8TKwAJKEvTqzNy2P1FKkvolIn4G7ET5fnJAZp7bwdj9gbOqsZdn5s79ibJ7EfF3wInAw8C6mfn4kEOSJOk5lhp2AJIkSZI04vaulgl8IDOfmWpAZs4Dtgd+SSnB/J2I6LaPrjSeTSjPycs6SeACVNtfSimp/KI+xCZJ6r9zmi5/IyLe1c6giNgP+FrTVWf3NKreOY1yItwqwGeGG4okSeMziStJkiRJw7UVJVl2bWbe1u6gzHyCUrL2WeDlwP59iU6j6nnV8u4ux99TLVfvQSySpME7FbiWckLOCsDZEXFlRLw/IuZGxJoRsUK1nBsRH4iIKykzcFegfLe5jtJLvHYy8yHgXZSZuEdExFciYrUhhyVJ0hLsiStJkiRJw/X8anlDy/WLGxciYvnMXNQ6MDP/GBG/BHagHIg8tW9RatQ8AKwNvKDL8etUywd7E44kaZAyc3FE7EGprPCS6uptGOt1O5WbgT0zc/GUWw5BRLynuvhF4BjgMOCvI+ISYB5wL/BEO/vKzDP6EqQkaeSZxJUkSZKk4WqUQW4tWdv8+xrAXROMv4mSxN28x3FptN1KOcFg54hYucOeuCsx1kfx1j7F1zMRsT2wH/A6YGNgNdo/XpKZ6bEVSbNSZt4VEdtSese+Z6rtm5wJHJ2Zf+5PZD1xGuVzqqEx43iP6qddCZjElST1heWUJUmSJGm4Hq6WK7Zcf3/T5U0nGd8o/ff8SbaROnVJtVwJOKnDsSdSegwCXNyziHosItaIiB8ClwPvB7YE1gTmUA7mt/sjSbNWZj6QmQcBWwCfBX4DPNWy2VPV9Z8FXpqZB9Y8gdsw3nt5J+//fg5IkvrKs0UlSZIkabhupsy0bS1b+7umyzsCP28dGBFLAa+pfn28H8FpZH0d+CiwHHBgNbv2yMxcONGAiFgL+AJlVivAk9V+aicilqEkqufiAXhJmlJm/oFSdhiAqn/sysCjVX/Zmeb0YQcgSdJUIjOn3kqSJEmS1BcR8RXgvcAdmblR0/WrAXcDywILgVdm5n0tYz8EfI5Syu8XmfnGgQWuWS8iPgl8mrFyk08BFwFXArcBj1Fm6m4IbAe8lZL0jWrMP2Tm8QMOuy0RcThwMiXOAL5H6Sl9HXB/Zj4zxPAkSZIkySSuJEmSJA1TRLwL+BYlmbRZZt7StO4sYP9q3Z2UMrW/o5Re3pPSn26pav0RmfnlwUav2S4ivgj8LWPJzskOIjTPaP1iZh7Zz9imIyIup/SSTkrfxk5LRkuSJElSX5nElSRJkqQhiohVgXsofTg/kZknNK1bD/gtsPpkuwDmAdtl5tP9jFWjKSL2BT4DbNLG5rcCH8/M7/Y3qumJiPuA5wE3ZubLhh2PJEmSJLUyiStJkiRJQxYRewPPBxZk5g9b1s0FzgM2nmD45cA7M/OevgapkRYRAewG7EzpI7s2VS9E4F5KGeJLgUtyBhxoiIgnKKXKT8/Mg4cdjyRJkiS1MokrSZIkSTUXEcsC7wB2AdYFFgO3AD/MzEuGGZs0E0XEzZQTI76ZmYcOORxJkiRJeg6TuJIkSZIkaaRExDnAO4FfZub2w45HktR7EfGG5t8z84qJ1k1H834lSeolk7iSJEmSJGmkRMROwM+AZ4GXZeZNQw5JktRjEbEYaBz8zsxcZoJ107HEfiVJ6iWTuJIkSZIkjaCI2HAQt5OZtw/idjoVEacAhwG/BnbJzMeHHJIkqYeqRG1DZubSE6ybjiX2K0lSL3mWkCRJkiT12agny1Rb8+nNLKTJJPU99nAEMAc4CJgXER8HLszMZ4YalSSpV65g4s+5ydZJklQLzsSVJEmSpD7rYcm+yVjOTx1pel5GH2+mljOUIuLSpl+3BZaj/C0WATcCD9HeazYzc5feRyhJkiRp1JnElSRJkqQ+G+VkmeorIuYzgFlImblJv2+jUxOcWNF4fbb7Nwl83UmSJEnqE8/SliRJkqT+ux1L9qlmMnPjYccwZBOdVNHPky0kSZIkqS0mcSVJkiSpz0yWSbWz07ADkCRJkqTJWE5ZkiRJkiRJkjSrRMSGg7idzLx9ELcjSRo9JnElSZIkSZIkSbPKBP3Pey0z02qXkqS+8ANGkiRJkiRJkjRb2etckjQjmcSVJEmSJEmSJM02t9PeTNx1KcfJG8neBB4CHgNWAlZrWfc08KeeRipJ0jgspyxJkiRJkkZeROwK7AS8BlgLWAV4BLgPuBa4NDN/OrwIJUm9FBFzgH8BjgIWA2cDZwBXZ+YjTdutAmwFHATsR0nofgE4JjOfGXDYkqQRYhJXkiRJkiSNrIjYm3IQf9M2Nr8Z+FhmXtDfqCRJ/RYRpwCHUk7W2Sszr2pjzHbABcCawKmZeVh/o5QkjTKTuJIkSZIkaSRFxOcpM7Cg/Z6JCZyYmR/pT1SSpH6LiDcBl1De03fNzEs7GLsL8JNq7Fsz8+L+RClJGnUmcSVJkiRJ0siJiGOAz1AOwgelx+GPgSuB+Yz1QtwIeD3wFmDZangCn8jMEwYbtSSpFyLiXGBf4LrM3LKL8fOAVwPnZea+vY5PkiQwiStJkiRJkkZMRKwP/AFYjpLA/QFweGbeNcmYdYCTgX2qq54ANs/MO/scriSpxyLiZmBj4LTMPKSL8adSeuTempntlOOXJKljSw07AEmSJEmSpAE7FFi+unxmZu49WQIXIDMXVrOtTquuWh7o+MC/JKkWXlgt53Q5fumW/UiS1HMmcSVJkiRJ0qh5c7V8FDi8w7FHAo9Ul9/Ss4gkSYPUeB/fqsvx21TLR3sQiyRJ4zKJK0mSJEmSRs0mlL62l2VmRwfgq+0vpZRhflEfYpMk9d/1lPfxzSLiXZ0MjIj9gc0onyPX9yE2SZIAk7iSJEmSJGn0PK9a3t3l+Huq5eo9iEWSNHjnNF3+RruJ3IjYD/ha01Vn9zQqSZKamMSVJEmSJEmj5oFq+YIux69TLR/sQSySpME7FbiWMht3BeDsiLgyIt4fEXMjYs2IWKFazo2ID0TElcBZ1fYJXAd8c2j3QJI060VmDjsGSZIkSZKkgYmIq4CtKb0M1+2kpHJErATcBawMXJOZ20wxRJJUQxGxLqU8/ksoSdm2hwJ/BHbKzAX9iE2SJHAmriRJkiRJGj2XVMuVgJM6HHsisEp1+eKeRSRJGqjMvAvYFjiDkpht9/GndlMAAATZSURBVOdMYBsTuJKkfnMmriRJkiRJGikRsQFwI7BcddX3gCMzc+EkY9YCvgDsRzmIvwjYPDPv6HO4kqQ+i4iXAIcAOwGvBJZtWv0U8FvgMuDUzLxx8BFKkkaRSVxJkiRJkjRyIuKTwKcZK6H5FHARcCVwG/AYZabuhsB2wFspSd+oxvxDZh4/4LAlSQMQEatRyuY/mpkPDTseSdJoMokrSZIkSZJGUkR8EfhbSlK2kZydcPOmy1/MzCP7GZskSZKk0WZPXEmSJEmSNJIy8wjgXcD86qrJeiAC3Aq80wSuJEmSpH5zJq4kSZIkSRppERHAbsDOwFxgbaoymsC9wHXApcAl6YEUSZIkSQNgEleSJEmSJEmSJEmSasRyypIkSZIkSZIkSZJUIyZxJUmSJEmSJEmSJKlGlhl2AJIkSZIkSb0WERsO4nYy8/ZB3I4kSZKk0WJPXEmSJEmSNOtExGKg3wc9MjM9QV6SJElSz/mPhiRJkiRJms1i2AFIkiRJUqdM4kqSJEmSpNnodvo/E1eSJEmS+sJyypIkSZIkSZIkSZJUI0sNOwBJkiRJkiRJkiRJ0hiTuJIkSZIkSZIkSZJUIyZxJUmSJEmSJEmSJKlGTOJKkiRJkiRJkiRJUo2YxJUkSZIkSZIkSZKkGjGJK0mSJEmSJEmSJEk1YhJXkiRJkiRJkiRJkmrEJK4kSZIkSZIkSZIk1YhJXEmSJEmSJEmSJEmqEZO4kiRJkiRJkiRJklQjJnElSZIkSZIkSZIkqUZM4kqSJEmSJEmSJElSjZjElSRJkiRJkiRJkqQaMYkrSZIkSZIkSZIkSTViEleSJEmSJEmSJEmSasQkriRJkiRJkiRJkiTViElcSZIkSZIkSZIkSaoRk7iSJEmSJEmSJEmSVCMmcSVJkiRJkiRJkiSpRkziSpIkSZIkSZIkSVKNmMSVJEmSJEmSJEmSpBoxiStJkiRJkiRJkiRJNWISV5IkSZIkSZIkSZJqxCSuJEmSJEmSJEmSJNWISVxJkiRJkiRJkiRJqhGTuJIkSZIkSZIkSZJUIyZxJUmSJEmSJEmSJKlGTOJKkiRJkiRJkiRJUo2YxJUkSZIkSZIkSZKkGjGJK0mSJEmSJEmSJEk1YhJXkiRJkiRJkiRJkmrEJK4kSZIkSZIkSZIk1YhJXEmSJEmSJEmSJEmqEZO4kiRJkiRJkiRJklQjJnElSZIkSZIkSZIkqUZM4kqSJEmSJEmSJElSjZjElSRJkiRJkiRJkqQaMYkrSZIkSZIkSZIkSTViEleSJEmSJEmSJEmSasQkriRJkiRJkiRJkiTViElcSZIkSZIkSZIkSaoRk7iSJEmSJEmSJEmSVCMmcSVJkiRJkiRJkiSpRkziSpIkSZIkSZIkSVKNmMSVJEmSJEmSJEmSpBoxiStJkiRJkiRJkiRJNWISV5IkSZIkSZIkSZJqxCSuJEmSJEmSJEmSJNWISVxJkiRJkiRJkiRJqhGTuJIkSZIkSZIkSZJUIyZxJUmSJEmSJEmSJKlGTOJKkiRJkiRJkiRJUo2YxJUkSZIkSZIkSZKkGjGJK0mSJEmSJEmSJEk1YhJXkiRJkiRJkiRJkmrEJK4kSZIkSZIkSZIk1YhJXEmSJEmSJEmSJEmqEZO4kiRJkiRJkiRJklQj/w/ihIDHxgOaYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "width=0.25\n",
    "address=np.arange(len(train_data.columns))\n",
    "tick_label=train_data.columns\n",
    "plt.figure(dpi=600,figsize=(3.6,1.5))\n",
    "plt.bar(address\n",
    "        ,xlf.feature_importances_\n",
    "        ,width\n",
    "        ,color='y'\n",
    "        ,label='xgboost')\n",
    "plt.bar(address+width\n",
    "        ,llf.feature_importances_/sum(llf.feature_importances_)\n",
    "        ,width\n",
    "        ,color='c'\n",
    "        ,label='lightgbm')\n",
    "plt.legend(fontsize=4)\n",
    "plt.xticks(address+width\n",
    "           ,tick_label\n",
    "           ,fontsize=4\n",
    "           ,rotation=90\n",
    "           ,horizontalalignment='center')\n",
    "plt.yticks(fontsize=4,rotation=0)\n",
    "plt.title('xgboost&lightgbm——feature_importances_',fontsize=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成提交文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['label'] = answers\n",
    "result=test[['loadingOrder', 'label']]\n",
    "test_data_path = 'A_testData0531.csv'\n",
    "test_data=pd.read_csv(test_data_path)\n",
    "test_data=get_data(test_data, mode='test')\n",
    "#\n",
    "test_data = test_data.merge(result, on='loadingOrder', how='left')\n",
    "test_data['ETA'] = (test_data['onboardDate'] + test_data['label'].apply(lambda x:pd.Timedelta(hours=x))).apply(lambda x:x.strftime('%Y/%m/%d  %H:%M:%S'))\n",
    "test_data.drop(['direction','TRANSPORT_TRACE'],axis=1,inplace=True)\n",
    "test_data['onboardDate'] = test_data['onboardDate'].apply(lambda x:x.strftime('%Y/%m/%d  %H:%M:%S'))\n",
    "test_data['creatDate'] = pd.datetime.now().strftime('%Y/%m/%d  %H:%M:%S')\n",
    "test_data['timestamp'] = test_data['temp_timestamp']\n",
    "# 整理columns顺序\n",
    "submit = test_data[['loadingOrder', 'timestamp', 'longitude', 'latitude', 'carrierName', 'vesselMMSI', 'onboardDate', 'ETA', 'creatDate']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loadingOrder</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>carrierName</th>\n",
       "      <th>vesselMMSI</th>\n",
       "      <th>onboardDate</th>\n",
       "      <th>ETA</th>\n",
       "      <th>creatDate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CF946210847851</td>\n",
       "      <td>2019-04-02T02:42:28.000Z</td>\n",
       "      <td>138.471062</td>\n",
       "      <td>40.278787</td>\n",
       "      <td>OIEQNT</td>\n",
       "      <td>R5480015614</td>\n",
       "      <td>2019/04/02  02:42:28</td>\n",
       "      <td>2019/04/24  05:11:26</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CF946210847851</td>\n",
       "      <td>2019-04-02T02:59:28.000Z</td>\n",
       "      <td>138.552168</td>\n",
       "      <td>40.327785</td>\n",
       "      <td>OIEQNT</td>\n",
       "      <td>R5480015614</td>\n",
       "      <td>2019/04/02  02:42:28</td>\n",
       "      <td>2019/04/24  05:11:26</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CF946210847851</td>\n",
       "      <td>2019-04-02T03:07:28.000Z</td>\n",
       "      <td>138.588250</td>\n",
       "      <td>40.352542</td>\n",
       "      <td>OIEQNT</td>\n",
       "      <td>R5480015614</td>\n",
       "      <td>2019/04/02  02:42:28</td>\n",
       "      <td>2019/04/24  05:11:26</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CF946210847851</td>\n",
       "      <td>2019-04-02T03:43:28.000Z</td>\n",
       "      <td>138.751325</td>\n",
       "      <td>40.459447</td>\n",
       "      <td>OIEQNT</td>\n",
       "      <td>R5480015614</td>\n",
       "      <td>2019/04/02  02:42:28</td>\n",
       "      <td>2019/04/24  05:11:26</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CF946210847851</td>\n",
       "      <td>2019-04-02T04:29:28.000Z</td>\n",
       "      <td>138.969782</td>\n",
       "      <td>40.581485</td>\n",
       "      <td>OIEQNT</td>\n",
       "      <td>R5480015614</td>\n",
       "      <td>2019/04/02  02:42:28</td>\n",
       "      <td>2019/04/24  05:11:26</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45451</th>\n",
       "      <td>XG479584941731</td>\n",
       "      <td>2019-01-13T03:56:08.000Z</td>\n",
       "      <td>104.633357</td>\n",
       "      <td>1.630708</td>\n",
       "      <td>JCMFTA</td>\n",
       "      <td>U2218600548</td>\n",
       "      <td>2019/01/10  00:27:58</td>\n",
       "      <td>2019/02/04  22:49:32</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45452</th>\n",
       "      <td>XG479584941731</td>\n",
       "      <td>2019-01-13T03:57:08.000Z</td>\n",
       "      <td>104.631958</td>\n",
       "      <td>1.626713</td>\n",
       "      <td>JCMFTA</td>\n",
       "      <td>U2218600548</td>\n",
       "      <td>2019/01/10  00:27:58</td>\n",
       "      <td>2019/02/04  22:49:32</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45453</th>\n",
       "      <td>XG479584941731</td>\n",
       "      <td>2019-01-13T03:57:38.000Z</td>\n",
       "      <td>104.631258</td>\n",
       "      <td>1.624615</td>\n",
       "      <td>JCMFTA</td>\n",
       "      <td>U2218600548</td>\n",
       "      <td>2019/01/10  00:27:58</td>\n",
       "      <td>2019/02/04  22:49:32</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45454</th>\n",
       "      <td>XG479584941731</td>\n",
       "      <td>2019-01-13T03:58:08.000Z</td>\n",
       "      <td>104.630597</td>\n",
       "      <td>1.622682</td>\n",
       "      <td>JCMFTA</td>\n",
       "      <td>U2218600548</td>\n",
       "      <td>2019/01/10  00:27:58</td>\n",
       "      <td>2019/02/04  22:49:32</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45455</th>\n",
       "      <td>XG479584941731</td>\n",
       "      <td>2019-01-13T03:59:08.000Z</td>\n",
       "      <td>104.629178</td>\n",
       "      <td>1.618552</td>\n",
       "      <td>JCMFTA</td>\n",
       "      <td>U2218600548</td>\n",
       "      <td>2019/01/10  00:27:58</td>\n",
       "      <td>2019/02/04  22:49:32</td>\n",
       "      <td>2020/06/10  16:33:35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45456 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         loadingOrder                 timestamp   longitude   latitude  \\\n",
       "0      CF946210847851  2019-04-02T02:42:28.000Z  138.471062  40.278787   \n",
       "1      CF946210847851  2019-04-02T02:59:28.000Z  138.552168  40.327785   \n",
       "2      CF946210847851  2019-04-02T03:07:28.000Z  138.588250  40.352542   \n",
       "3      CF946210847851  2019-04-02T03:43:28.000Z  138.751325  40.459447   \n",
       "4      CF946210847851  2019-04-02T04:29:28.000Z  138.969782  40.581485   \n",
       "...               ...                       ...         ...        ...   \n",
       "45451  XG479584941731  2019-01-13T03:56:08.000Z  104.633357   1.630708   \n",
       "45452  XG479584941731  2019-01-13T03:57:08.000Z  104.631958   1.626713   \n",
       "45453  XG479584941731  2019-01-13T03:57:38.000Z  104.631258   1.624615   \n",
       "45454  XG479584941731  2019-01-13T03:58:08.000Z  104.630597   1.622682   \n",
       "45455  XG479584941731  2019-01-13T03:59:08.000Z  104.629178   1.618552   \n",
       "\n",
       "      carrierName   vesselMMSI           onboardDate                   ETA  \\\n",
       "0          OIEQNT  R5480015614  2019/04/02  02:42:28  2019/04/24  05:11:26   \n",
       "1          OIEQNT  R5480015614  2019/04/02  02:42:28  2019/04/24  05:11:26   \n",
       "2          OIEQNT  R5480015614  2019/04/02  02:42:28  2019/04/24  05:11:26   \n",
       "3          OIEQNT  R5480015614  2019/04/02  02:42:28  2019/04/24  05:11:26   \n",
       "4          OIEQNT  R5480015614  2019/04/02  02:42:28  2019/04/24  05:11:26   \n",
       "...           ...          ...                   ...                   ...   \n",
       "45451      JCMFTA  U2218600548  2019/01/10  00:27:58  2019/02/04  22:49:32   \n",
       "45452      JCMFTA  U2218600548  2019/01/10  00:27:58  2019/02/04  22:49:32   \n",
       "45453      JCMFTA  U2218600548  2019/01/10  00:27:58  2019/02/04  22:49:32   \n",
       "45454      JCMFTA  U2218600548  2019/01/10  00:27:58  2019/02/04  22:49:32   \n",
       "45455      JCMFTA  U2218600548  2019/01/10  00:27:58  2019/02/04  22:49:32   \n",
       "\n",
       "                  creatDate  \n",
       "0      2020/06/10  16:33:35  \n",
       "1      2020/06/10  16:33:35  \n",
       "2      2020/06/10  16:33:35  \n",
       "3      2020/06/10  16:33:35  \n",
       "4      2020/06/10  16:33:35  \n",
       "...                     ...  \n",
       "45451  2020/06/10  16:33:35  \n",
       "45452  2020/06/10  16:33:35  \n",
       "45453  2020/06/10  16:33:35  \n",
       "45454  2020/06/10  16:33:35  \n",
       "45455  2020/06/10  16:33:35  \n",
       "\n",
       "[45456 rows x 9 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "submit.to_csv('result6_11v1.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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